Seminars

Abstract

Python is a popular programming language used worldwide. CPython is its reference implementation, written in C. There has been a multi-year effort to speed up CPython, starting from version 3.11. In this talk, I'll cover what we've done. Specifically, this talk will cover specializing/quickening interpreters, inline caching, JIT compilers, abstract interpretation, and DSLs (domain-specific languages). The key takeaway from this talk is how CPython uses DSLs and abstract interpretation all the way down to reduce maintenance burden and developer error.

Bio

Ken Jin is a Python/CPython core developer and a student at the National University of Singapore. He is also a part-time software engineer at Quansight Labs. He recently wrote CPython's Tier 2 optimizer (JIT optimizer) in CPython 3.13. At Quansight Labs, he previously worked for a brief period of time on TorchDynamo, PyTorch 2's JIT compiler, and is now working on CPython performance work in multi-threaded environments.

Abstract

Bringing the promise of quantum computation into reality requires not only building a quantum computer but also correctly programming it to run a quantum algorithm. To obtain asymptotic advantage over classical algorithms, quantum algorithms rely on the ability of data in quantum superposition to exhibit phenomena such as interference and entanglement. In turn, an implementation of the algorithm as a program must correctly orchestrate these phenomena in the states of qubits. Otherwise, the algorithm would yield incorrect outputs or lose its computational advantage.

Given a quantum algorithm, what are the challenges and costs to realizing it as an executable program? In this talk, I answer this question by showing how basic programming abstractions – such as data structures and control flow – upon which many quantum algorithms rely can fail to work correctly or efficiently on a quantum computer. I then show how we can leverage insights from programming languages to re-invent these abstractions to meet the demands of quantum algorithms. This approach holds out a promise of expressive and efficient tools for programming a quantum computer.

Bio

Charles Yuan is a Ph.D. student at MIT CSAIL working with Prof. Michael Carbin. His current research examines the challenges of programming quantum computers and other emerging models of computation. His work has appeared in the ACM SIGPLAN POPL, OOPSLA, and PLDI conferences and has been recognized with the SIGPLAN Distinguished Artifact Award and the CQE-LPS Doc Bedard Fellowship.

Abstract

WebAssembly (Wasm) is a simple, formally specified, low-level bytecode format that is designed to be a good compilation target for statically-typed languages such as C, C++, and Rust (among others). It has excellent sandboxing properties, and has thus seen adoption in applications where untrusted user code must run. Unfortunately, the story for dynamically-typed (and otherwise highly dynamic) languages such as JavaScript running on Wasm-based platforms is less well-developed: the state of the art is to embed an interpreter within a Wasm module and package it with bytecode.

In this talk, I walk through (i) background on Wasm-based platforms and why they are attractive; (ii) difficulties that Wasm, as a highly static environment, presents to languages traditionally accelerated by a JIT compiler; and (iii) outline one path to achieve ahead-of-time compilation of such languages to Wasm modules without JIT.

The key insight of this work is that WebAssembly provides a novel opportunity to apply partial evaluation to get a compiler "for free" out of an interpreter. I will outline how we first developed and upstreamed a new high-performance interpreter tier in the SpiderMonkey JS engine, then developed a partial evaluator called "weval" for Wasm that, together with the upstreamed interpreter with minimal changes, produces fully ahead-of-time-compiled code from JS source, encapsulating all remaining dynamism in the inline-cache mechanism. This results in a ~2x speedup overall, with ~4x on some microbenchmarks and potential for higher.

Bio

Chris is a software engineer at Fastly, where he works on WebAssembly-related technology involving Cranelift. Previously, he was at Mozilla, where he worked on Firefox's SpiderMonkey JS just-in-time compiler and Cranelift. Before joining Mozilla, he earned a PhD at Carnegie Mellon University, where he studied compilers, static and dynamic analysis, and before that, CPU microarchitecture.

Abstract

Separation is a powerful concept when verifying programs that manipulate memory, and concurrent programs. In the probabilistic setting, there are also natural notions of separation. For instance, probabilistic independence is useful for describing the result of random sampling---a basic operation in all probabilistic languages---and for reasoning about groups of random variables. Nevertheless, existing verification methods handle independence poorly, if at all.

I will survey our recent work on probabilistic separation logics, based on probabilistic models of the logic of bunched implication (BI) and other bunched logics. In the program logic PSL, separation models probabilistic independence; we have used PSL to verify information-theoretic security of some well-known cryptographic constructions. I'll also describe extensions to reason about other separation-like relations between random variables, like conditional independence and negative association.

Bio

Justin Hsu is an assistant professor of Computer Science at Cornell University. He was previously an assistant professor at UW--Madison and a postdoc at Cornell and UCL, after receiving his doctorate in Computer Science from the University of Pennsylvania. His research focuses on formal verification of probabilistic programs, including algorithms from differential privacy, protocols from cryptography, and mechanisms from game theory. His work has been recognized by an NSF CAREER award, a SIGPLAN John C. Reynolds Doctoral Dissertation award, distinguished paper awards from POPL and CAV, and industrial awards from Facebook.

Abstract

In this talk I will describe some recent work on the P4 programming language, a domain-specific language for programming networking accelerators. First, I will describe Petr4 (pron. "petra"), our semantics for P4, and give a brief overview of the language's syntax, type system, and operational semantics. Then I will cover Leapfrog, a Coq-based framework for verifying the equivalence of packet parsers defined in P4. Leapfrog is based on P4 Automata (P4A), an automata-theoretic model based on Petr4's semantics of packet parsers. We provide a decision procedure for P4A equivalence based on symbolic bisimilarity; it is implemented in Coq as a tactic and proved sound with respect to the operational semantics of P4A. The tactic relies on a trusted SAT solver to decide symbolic entailments. We find the bisimilarity algorithm generalizes to certain relational properties beyond mere equivalence.

Bio

Ryan Doenges is a Ph.D. candidate at Cornell University, where he studies programming language principles for emerging networking devices. He is advised by Nate Foster.

Abstract

Noninterference guarantees that an attacker cannot infer secrets by interacting with a program. An information flow control (IFC) type system asserts noninterference by tracking the level of information learned and disallowing leakage to parties of lesser or unrelated level. In this talk, I explore session types as an enabler for tracking the flow of information. Session types stipulate according to which protocol messages must be exchanged along channels connecting concurrently executing processes (or threads). I develop an IFC type system for linear session-typed process calculus that employs possible worlds from hybrid logic to reason about security levels. To prove that well-typed programs in the resulting language ensure noninterference, I develop a logical relation. I survey what the main challenges in the development are --- non-termination (because of general recursive types) and non-determinism (because of concurrency) --- and explain how to address them. Distinguishing features of the resulting logical relation are its interactive nature (the program interacts with its context) and the use of an observation index (as opposed to a step index or later modality). The latter ensures compositionality despite concurrency.

Bio

Stephanie Balzer is an Assistant Research Professor in the Principles of Programming group at CMU. Her research is concerned with compositional verification of concurrent programs, using logic and type systems, logical relations, and verification logics.

Abstract

GPUs offer parallelism as a commodity, but they are difficult to programcorrectly. Static analyzers that guarantee data-race freedom (DRF) areessential to help programmers establish the correctness of theirprograms (kernels). However, existing approaches produce too many false alarms and struggle to handle larger programs. To address these limitations we formalize a novel compositional analysis for DRF, based on access memory protocols. These protocols are behavioral types that codify the way threads interact over shared memory. Our work includes fully mechanized proofs of our theoretical results, the first mechanized proofs in the field of DRF analysis for GPU kernels. Our theory is implemented in Faial, a tool that outperforms the state-of- the-art. Notably, it can correctly verify at least 1.42× more real-world kernels, and it exhibits a linear growth in 4 out of 5 experiments, while others grow exponentially in all 5 experiments

This talk presents the CAV21 publication and reports updates on this research (journal and workshop publications)

Bio

Tiago Cogumbreiro joined the Department of Computer Science at the University of Massachusetts Boston in fall 2018 as an assistant professor. Prior to that, Tiago was a postdoctoral researcher at the Georgia Institute of Technology and Rice University, supervised by Professor Vivek Sarkar, and a research assistant at Imperial College London, supervised by Professor Nobuko Yoshida. Tiago obtained a PhD from the University of Lisbon, Portugal.

Abstract

Val is a zero-cost abstraction language based on two of the best ideas behind C++: generic programming and first-class value semantics. Unlike C++, however, Val achieves memory safety without resorting to defensive copying.

In this talk, I will explain how Val codifies best practices from modern C++ into a type system that guarantees type and memory safety while being able to express 99% of the low-level bit pushing one can write in C++ with the same predictable efficiency. I will also attempt to convince you that you don't need neither pointers, references, nor sophisticated lifetime annotations to get there.

Bio

Abstract

Specialized, application-specific hardware accelerators are chipping away at the dominance of traditional, general-purpose CPUs. We need to make it possible for domain experts—not just hardware experts—to harness the efficiency of hardware specialization for the computations they care about. Domain-specific languages (DSLs) for building hardware accelerators offer a way to raise the level of abstraction in hardware design from gates, wires, and clock cycles. Unfortunately, building a new hardware DSL is a gargantuan task requiring not only the design of new abstractions, but also supporting tools such as an optimizing compiler, testing, and debugging infrastructure.

Our solution is Calyx (calyxir.org), an intermediate language and a compiler infrastructure that can represent, optimize, and lower accelerators to synthesizable hardware designs. By targeting Calyx instead of a traditional hardware design language, designers can build new DSLs and generate a custom hardware accelerator without needing hardware expertise.

Bio

Rachit Nigam (rachitnigam.com) is PhD candidate at Cornell University interested in programming languages, computer architectures, and compilers that turn programs to architectures.

Abstract

"In this talk I will present PaSh, a system that exposes data parallelism in POSIX shell scripts. To achieve that, PaSh offers: (i) an order-aware dataflow model that precisely captures a fragment of the shell, (ii) a set of dataflow transformations that extract parallelism and have been proven to be correct, and (iii) a lightweight framework that captures the correspondence of shell commands and order-aware dataflow nodes. PaSh achieves orders-of-magnitude performance improvements on a variety of scripts found in the wild. PaSh is open-source https://github.com/binpash/pash and hosted under the Linux Foundation. The talk that I will be giving roughly corresponds to two publications, one at EuroSys 2021 (which also received the best paper award) and one at ICFP 2021."

Bio

Excerpt from website:

"I am a PhD student at the University of Pennsylvania, where I am fortunate to be advised by Rajeev Alur. My research interests lie in the intersection of programming languages, distributed systems, and software verification. I have done an internship in Microsoft Research, where I worked with Sebastian Burckhardt, and an internship in the Automated Reasoning Group in AWS, where I worked with Daniel Schwartz-Narbonne. Before my PhD, I did my undergraduate studies at the National Technical University of Athens, where I was advised by Kostis Sagonas."

Abstract

Programming languages are simultaneously formal systems and user interfaces with which programmers work. Unfortunately, programmers have a hard time writing safe software: serious bugs and security vulnerabilities are common. In many cases, however, languages with strong safety guarantees have been hard to use, and safer languages have seen slow adoption. In this talk, I’ll discuss user-centered design methods I developed to help language designers create languages that are easier to use. I’ll show how I created and evaluated Glacier, an extension for Java that enforces immutability, Obsidian, a new smart contract language that uses a linear type system, and Bronze, a new garbage collector for Rust. In each case, I found that the resulting language helps programmers write software more effectively than with prior approaches.

Bio

Excerpt from website:

"I completed my Ph.D. in the Carnegie Mellon University Computer Science Department. I was a student of Jonathan Aldrich and Brad A. Myers. I also collaborated closely with Joshua Sunshine. Now, I’m a postdoctoral fellow at the University of Maryland. I work with Michael Hicks and Adam Porter."

"I spent ten years at Apple. For eight years, I was a full-time software engineer on the iWork team, focusing on Numbers. I worked on versions for macOS, iOS, and iCloud. If you’re still using some other spreadsheet app, give Numbers a try!"

Abstract

Staging -- splitting a program into stages, the code generator and the generated code -- can mechanically turn an interpreter into a compiler. Going further, we can collapse a tower of interpreters into a one-pass compiler. An interpreter written in a relational programming language (miniKanren) for a functional programming language (subset of Racket) can not only evaluate an expression to a value, but also synthesize an expression (or more generally, term) for a hole -- in particular, turning functions into relations. We adapt staging to the relational setting, turning functions into relations -- and more -- with reduced interpretive overhead, promising a tractable avenue for the synthesis of functional programs.

Bio

"Nada Amin is an Assistant Professor of Computer Science at Harvard SEAS. Previously, she was a University Lecturer in Programming Languages at the University of Cambridge; a member of the team behind the Scala programming language at the Ecole Polytechnique Federale de Lausanne (EPFL), where she pursued her PhD; and a software engineer at Google, on the compiler infrastructure supporting Gmail and other Google Apps. She holds bachelor of science and master of engineering degrees from the Massachusetts Institute of Technology (MIT)."

Abstract

Shared session types generalize the Curry-Howard correspondence between intuitionistic linear logic and the session-typed pi-calculus with adjoint modalities that mediate between linear and shared session types, giving rise to a programming model where shared channels must be used according to an acquire-release discipline. The resulting language recovers the expressiveness of the untyped asynchronous pi-calculus and accommodates practical programming scenarios, such as shared servers, file systems etc. The resulting language is implemented as a prototype in C0 (a safe subset for C used to teach freshmen at CMU) and as a DSL in Rust.

The generalization, however, comes at the cost of deadlock-freedom, a property which holds for the purely linear fragment. In this talk, I report on recent work that makes up for this deficiency and introduces a type system of linear and shared session types in which the adjoint modalities are complemented with possible worlds to rule out cyclic dependencies between acquires and synchronizations. I distill the key abstractions to reason about cyclic dependencies and the key invariants enforced by the type system to guarantee deadlock-freedom. Time permitted, I report on ongoing work that also uses possible world modalities for type-based verification of program properties of interest.

Bio

Stephanie Balzer is a research faculty in the Principles of Programming group in the Computer Science Department at Carnegie Mellon University. Stephanie obtained her PhD from ETH Zurich under the supervision of Thomas R. Gross. In her PhD work, Stephanie developed the sequential relationship-based programming language Rumer and an invariant-based verification technique for Rumer. Stephanie’s current work focuses on the development of type systems and logics for verifying properties of concurrent programs.

Abstract

This talk tells the rather uncommon story of the co-evolution of my company, Raincode, with a home-grown programming language, YAFL. The two are intimately intertwined: Raincode would crumble if YAFL was to disappear, and YAFL would have no reason to exist if Raincode was to stop its activities. YAFL is a strongly-typed object-oriented language originally designed and implemented a quarter of century ago. Our story starts with YAFL’s inception, its design, our motivations (including the usual fantasies of world domination) and the technical underpinnings and some key implementation decisions.

Our story is rooted in the real-world where pragmatics reign. I will argue that out design choices and implementation decisions have converged to give YAFL its true value, and, that the fruits of growing our own language are well worth the maintenance burden. The talk will argue that YAFL is a non-DSL, and touch on the good ideas as well as the lousy ones. I will discuss how to evolve a language over time by judiciously adding, as well as removing, features. Above all, it is story about people. The language designer, its implementors, its first users, even its customers are the main. Their interplay sheds a light on how languages are created and maintained.

Bio

Darius Blasband was born in 1965 and has a PhD from the Université Libre de Bruxelles. His focus is legacy code modernization, articulated around compilers for legacy languages. He founded Raincode (https://www.raincode.com), and designed its core technology. Above all, Darius is a geek. Building software systems is what he likes best—after his family, chocolate, and on par with music.

Abstract

Automatic differentiation (AD) is the science of efficiently computing the derivative (or gradient, or Jacobian) of functions specified by computer programs. It is a fundamental tool in several fields, most notably machine learning, where it is the key for training deep neural networks. Albeit AD techniques natively focus on a restricted class of programs, namely first-order straight-line programs, the rise of so-called differentiable programming in recent years has urged for the need of applying AD to complex programs, endowed with control flow operators and higher-order combinators, such as map and fold. In this talk, I will discuss the extension of AD algorithms to PCF, a(n idealized) purely functional programming language. We will first consider the simply-typed lambda-calculus, showing in particular how linear negation is related to reverse-mode AD (a.k.a., backpropagation), and then see how the extra features of PCF, namely full recursion and conditionals, may be dealt with, stressing the difficulties posed by the latter.

Joint work with Aloïs Brunel (Deepomatic) and Michele Pagani (IRIF, Université de Paris).

Bio

Damiano Mazza is chargé de recherche (researcher) at CNRS, working at the CS lab of Université Sorbonne Paris Nord, a position he has held since 2008. He obtained his Ph.D. in 2006 at the Institut de Mathématiques de Luminy, in Marseille. Before that, he studied CS Engineering in Rome, Italy, which is where he is from. His research interest are at the interface between logic in computer science, the theory of programming languages and complexity theory, with a bias towards the viewpoint provided by linear logic and category theory.

Abstract

In this talk, I will give a high-level overview of CertiCoq, a verified compiler from Coq to C, focusing on my research on CertiCoq's backend. The backend of CertiCoq is based on a micro-pass compiler architecture: each stage in its pipeline performs a simple task, yet they combine to generate efficient code. The micro-pass design along with our proof framework based on logical relations greatly facilities modular reasoning and proof reuse. Furthermore, I will show how this logical relation framework can be extended to provide correctness guarantees for programs compiled separately with CertiCoq, using perhaps different sets of optimizations. Lastly, I will discuss future research directions with CertiCoq, such as provably correct interoperation of Coq and C using the Verified Software Toolchain.

Bio

Zoe Paraskevopoulou is a 5th year PhD student at Princeton University, working with professor Andrew Appel. Prior to that she obtained her master's degree from École normale supérieure Paris-Saclay and her undergraduate degree from National Technical University of Athens. She has done several internships at institutes such as INRIA Paris, Max Planck Institute of Software Systems, Microsoft Research and Facebook.

Abstract

Serverless computing is a cloud computing abstraction that makes it easier to write robust, large-scale web services. In serverless computing, programmers write what are called serverless functions, and the cloud platform transparently manages the operating system, resource allocation, load-balancing, and fault tolerance. When demand spikes, the platform automatically allocates additional hardware and manages load-balancing; when demand falls, the platform silently deallocates idle resources; and when the platform detects a failure, it transparently retries affected requests.

In this talk, we will see how language-based techniques can make serverless computing faster, safer, and cheaper for programmers. First, we present a detailed, operational model of a serverless computing platform, which shows that serverless computing is a leaky abstraction. We then derive a less-leaky "naive semantics" and precisely characterize how the two semantics coincide. Second, we extend the operational semantics with a domain-specific language for composing serverless functions, and show that the language design lends itself to an efficient implementation that lowers the cost of building modular applications. Finally, we present a "serverless function accelerator" that significantly reduces the latency of a large class of functions using language-based sandboxing and speculative optimizations.

Bio

Arjun Guha is an associate professor of Computer Science at the University of Massachusetts Amherst. Using the tools and techniques of programming languages, his research addresses security, reliability, and performance problems in web applications, systems, networking, and robotics. He received a PhD in Computer Science from Brown University in 2012 and a BA in Computer Science from Grinnell College in 2006.

Abstract

Rust is a young and actively developed "systems programming language" that promises to overcome the seemingly fundamental tradeoff between high-level safety guarantees and low-level control over resource management. This talk is about my research on understanding and evolving this groundbreaking new language.

The first part of the talk is about building formal foundations for the existing Rust language. Rust employs a strong, ownership-based type system to rule out common systems programming anomalies, but then extends the expressive power of this core type system through libraries that internally use unsafe features. Unfortunately, this ``extensible'' aspect of the Rust language makes Rust's safety claims rather non-obvious, and there is good reason to question whether they actually hold. To address this, we have developed RustBelt, the first formal (and machine-checked) safety proof for a language representing a realistic subset of Rust. Our proof supports the extensible nature of Rust's safety story in the sense that, for each new Rust library that uses unsafe features, we can say what verification condition it must satisfy in order for it to be deemed a safe and compositional extension to the language.

The second part is about evolving the semantics of Rust to better support compiler optimizations. In particular, Rust's reference types provide strong aliasing guarantees, which a clever optimizing compiler should be able to exploit. However, if no care is taken, unsafe code (which plays an essential role in the Rust ecosystem, as mentioned above) can subvert these guarantees. To address this, we have developed Stacked Borrows, a new operational semantics for memory accesses in Rust. On the one hand, Stacked Borrows defines an aliasing discipline and declares programs violating it to have undefined behavior, thus making it possible for us to formally verify the soundness of a number of useful type-based intraprocedural optimizations. On the other hand, we have also implemented the Stacked Borrows semantics in Miri, an interpreter for Rust, and run large parts of the Rust standard library test suite in Miri. In so doing, we have validated that our semantics is sufficiently permissive to account for real-world unsafe Rust code.

Bio

Ralf Jung is a PhD student at MPI-SWS in Saarbrücken, where he previously completed his undergraduate degree with Saarland University. Ralf’s research interests center around separation logic, operational semantics for low-level languages, (semantic) type systems, and mechanized+modular proof for program verification. Ralf also loves to learn about anything people do with Rust and the ways in which formal methods could help.

Abstract

Nom demonstrated that efficient and well-behaved gradual typing is possible. But it did so by assuming all code was written using nominal patterns in a simplistic type system. The nominal patterns limit the applicability to existing untyped code, and the simplistic type system limits the applicability to modern industry languages. This talk will present recent results addressing both of these limitations. I will illustrate an implemented design that enables structural patterns to mix with nominal patterns while also providing a variant of the gradual guarantee that ensures interoperability between these patterns and a semantics-preserving migration path from structural patterns into nominal patterns. I will further demonstrate that Nom's design strategy scales to parametric polymorphism, where the key challenge is not actually generic classes or interfaces but rather generic methods and type-argument inference. Although preliminary, experiments suggest that these more expressive designs can still be implemented relatively efficiently, altogether indicating that gradual typing is a viable feature for nominal languages.

Bio

Ross Tate is an Assistant Professor in Computer Science at Cornell University and the Director of the Single Open Intermediate Language Initiative. His research combines theoretical foundations with studies of programmer behavior to develop language designs that ascribe to both academic and industrial principles. He collaborates with major programming languages such as Kotlin, WebAssembly, Julia, Ceylon, and Java to inform his work from practice and incorporate his work into practice. Due to his research contributions and industry impact, he was awarded the Dahl Nygaard Junior Prize in 2017.

Abstract

Asymmetric Numeral Systems (ANS) are an entropy-based encoding method introduced by Jarek Duda in 2007, combining the Shannon-optimal compression effectiveness of arithmetic coding with the execution efficiency of Huffman coding. Existing presentations of the ANS encoding and decoding algorithms are somewhat obscured by the lack of suitable presentation techniques; I will present an equational derivation, calculational where it can be, and highlighting the creative leaps where it cannot. This is work to appear at Mathematics of Program Construction in October.

Bio

Jeremy Gibbons is Professor of Computing at the University of Oxford, and head of the Algebra of Programming research group there. His research interests are in programming languages, especially functional programming. He is Editor-in-Chief of the Journal of Functional Programming, Past Chair of IFIP WG2.1 on Algorithmic Languages and Calculi, and Past Vice Chair of ACM SIGPLAN.

Abstract

The Internet of Things (IoT) has the potential for being the next industrial revolution, envisioning billions of embedded devices. Because of their limited resources, IoT devices present specific challenges to their programming and deployment. For instance, limited battery lifetime imposes extreme energy-savings efforts, and low RAM and CPU processing power not only restrict computation power but can also lead to unexpected runtime errors (e.g, out of memory).

C and C++ languages are often preferred when programming these devices. These languages deliver good performance but are notoriously unsafe and ill-suitable for proving high-level properties specific to HW usage (e.g., sending a message only when Wi-Fi module is connected). Invalid program behaviors can lead to silent errors and energy wasting, resulting in the high-cost of reprogramming thousands of devices.

In this presentation, I will introduce Pekoe, a language for programming IoT devices that can determine, at compile time, the correct usage of hardware components. Pekoe introduces Harel Statecharts as types to model the hardware behavior. The compiler checks every possible execution trace of a program against the hardware model (the type) thus ensuring the correct order of system calls (e.g., sending a message via Wi-Fi, or suspending the CPU).

Bio

Carlos Mão de Ferro is a visiting Fulbright scholar at UMass Boston (February to July 2019), and is a PhD candidate in Computer Science, University of Lisbon, Portugal.

His research focuses on new programming languages techniques that deliver a safe and resource-aware programming of embedded devices, thus promoting a reliable and sustainable Internet of Things (IoT) environment.

Carlos has been working on WSNs and embedded software for 10 years. After his master thesis conclusion in 2013, he alternated between the academia and the industry worlds. Carlos developed a smart irrigation system for the Green by Web startup that won the Smart Open Lisboa Startup contest in 2017.

In general, Carlos is curious about Science and driven by the desire to understand how things work and how discoveries and inventions come to see the light of day. He enjoys reading topics about Physics, Astronomy and Biology. He is also passionate about music, having learned to play the flute and the piano as a child.

During his stay at UMB, Carlos will be particularly interested in foster collaborations with researchers from IoT-related areas.

Abstract

Recently, there has been a lot of research on shared-memory concurrency. Nevertheless, programmers are discouraged from using it because of the difficulty of writing clear, correct programs. This is embodied for example in the Go Language slogan “Do not communicate by sharing memory; instead, share memory by communicating.” But do we have the right abstractions for message-passing concurrent programming? I argue that we do not (yet!) because concurrency constructs are usually bolted on to an existing language with an entirely different semantic foundation. I will review some recent progress on designing better abstractions based on strong logical and type-theoretic principles. Multiple themes from functional and object-oriented programming will re-emerge from these foundations in a new form, including (ha!) shared memory.

Bio

Frank Pfenning studied Mathematics and Computer Science at the Technical University Darmstadt and then left for Carnegie Mellon University on a Fulbright scholarship where he obtained his Ph.D. in Mathematics in 1987 under the supervision of Professor Peter Andrews. He subsequently joined the Department of Computer Science at Carnegie Mellon University as research faculty where he became Professor in 2002 and served as Director of Graduate Programs from 2004 to 2008 and Associate Dean for Graduate Education from 2009 to 2010. He was the Joseph F. Traub Professor of Computer Science and Head of the Computer Science Department from 2013 to 2018. He has spent time as visiting scientist at the Max-Planck-Institute for Computer Science in Saarbrücken, as Alexander-von-Humboldt fellow at the Technical University Darmstadt, and as visiting professor at École Polytechnique and INRIA-Futurs. He has advised 24 completed Ph.D. theses and won the Herbert A. Simon Award for Teaching Excellence in the School of Computer Science in 2002. He served as trustee, vice president, and president of CADE, Inc., the governing body of the International Conference on Automated Deduction, and on advisory boards for INRIA, the Max-Planck-Institute for Computer Science, and Seoul National University. He has chaired several conferences and program committees, including CADE and LICS, and has been a member of the editorial boards for Theoretical Computer Science, Journal of Automated Reasoning, and the Journal of Symbolic Computation. He was named Fellow of the ACM in 2015. His research interests include programming languages, logic and type theory, logical frameworks, automated deduction, and computer security. In his spare time he enjoys playing squash, running, hiking, cooking, and reading.

Abstract

The scale and pervasiveness of modern software poses a challenge for programmers: software reliability is more important than ever, but the complexity of computer systems continues to grow. Automated programming tools are powerful weapons for programmers to tackle this challenge: verifiers that check software correctness, and synthesizers that generate new correct-by-construction programs. These tools are most effective when they apply domain-specific optimizations, but doing so today requires considerable formal methods expertise. In this talk, I present a new application-driven approach to optimizing the automated programming stack underpinning modern domain-specific tools. I will demonstrate the importance of programming tools in the context of memory consistency models, which define the behavior of multiprocessor CPUs and whose subtleties often elude even experts. Our new tool, MemSynth, automatically synthesizes formal descriptions of memory consistency models from examples of CPU behavior. We have used MemSynth to synthesize descriptions of the x86 and PowerPC memory models, each of which previously required person-years of effort to describe by hand, and found several ambiguities and underspecifications in both architectures. I will then present symbolic profiling, a new technique we designed and implemented to help people identify the scalability bottlenecks in automated programming tools. These tools use symbolic evaluation, which evaluates all paths through a program, and is an execution model that defies both human intuition and standard profiling techniques. Symbolic profiling diagnoses scalability bottlenecks using a novel performance model for symbolic evaluation that accounts for all-paths execution. We have used symbolic profiling to find and fix performance issues in 8 state-of-the-art automated tools, improving their scalability by orders of magnitude, and our techniques have been adopted in industry. Finally, I will give a sense of the importance of future application-driven optimizations to the automated programming stack, with applications that inspire improvements to the stack and in turn beget even more powerful automated tools.

Bio

James Bornholt is a Ph.D. candidate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, advised by Emina Torlak, Dan Grossman, and Luis Ceze. His research interests are in programming languages and formal methods, with a focus on automated program verification and synthesis. His work has received an ACM SIGPLAN Research Highlight, two IEEE Micro Top Picks selections, an OSDI best paper award, and a Facebook Ph.D. fellowship.

Abstract

Graph-manipulating programs such as garbage collectors are notoriously unpleasant to reason about, which is unfortunate since such programs are also used in critical algorithms and systems. We show how to verify such programs written in real C in a fully machine-checked context.

Bio

Aquinas Hobor is an Assistant Professor with a joint appointment at Yale-NUS College and the School of Computing, National University of Singapore. From 2008-2011 he was a Lee Kuan Yew Postdoctoral Fellow after getting his PhD from Princeton University. He grew up near Chicago and his Go ranking is approximately 1-dan.

Abstract

I will divide my presentation in two short talks. In the first part of my presentation, I will describe how static analysis of C/C++, our lab's initial expertise, widely differs from static analysis of Java EE, and describe some of the challenges we encountered in the Wafer project. In the second part of my talk, I will describe how we scale dynamic security analysis for Node.js with taint inference, and how the Affogato project compares to state-of-the-art.

Bio

François Gauthier graduated in 2014 from Polytechnique Montreal with a PhD in Software Engineering. The same year, he joined the program analysis team at Oracle Labs Australia, under the direction of Dr. Cristina Cifuentes, to start and lead the Wafer project for static vulnerability detection in Java EE. In June 2017, he transitioned to, and is now leading the Affogato project for dynamic security analysis of Node.js. Apart from application security, François is also exploring program analysis and machine learning approaches to detect document-based malware as well as fuzzing techniques to automatically generate test inputs.

Abstract

Constant-time programming is an established discipline to secure programs against timing attackers. Several real-world secure C libraries such as NaCl, mbedTLS, or Open Quantum Safe, follow this discipline. We propose two advanced compile-time verification techniques to enforce this discipline. The first one operates at assembly level with help from alias informations that are computed at source level and transmitted by the compiler. The second one operates at source level, using advanced abstract interpretation techniques. For this last approach, we also study how to ensure that the compiler will not break the property during compilation. These techniques have been implemented in the CompCert compiler toolchain and tested on several crypto libraries. These works have been presented at CCS’14, CSF’17 and ESORICS’17.

Bio

I am professor of Computer Science and head of the Department of Computer Science at ENS Rennes. My research activities take place in the Celtique team (joint team between University of Rennes, ENS Rennesand Inria Rennes, under the IRISA joint lab). I received a Ph.D. in Computer Science from the University of Rennes, France, in 2005, and a Habilitation à diriger les recherches in Computer Science from the ENS Cachan, France, in 2012. I joined ENS Cachan in September 2013 as full professor (in 2014, the brittany extension of ENS Cachan has becomed ENS Rennes). Between 2007 and 2013, I was a full research at INRIA Rennes research center. Earlier, I was holding a postdoc position at INRIA Sophia-Antipolis under the supervision of Gilles Barthe. In the 2011-13 academic years, I took a sabbatical and visited Jan Vitek's group at Purdue University, Indiana, USA, during the first year, and then Greg Morrisett's group at Harvard University, Cambridge, USA, during the second year. My research interests include formal methods, programming languages, program verification, software, and system security. I am a long time happy user of the Coq proof assistant and the theory of Abstract interpretation. More recently I have been conducting several researches about the verified C compiler CompCert. My research is currently funded by an ERC Consolidator Grant "VESTA: VErified STatic Analysis platform" (2018-2023).

Abstract

The precision of heap analyses determines the precision of several associated optimizations, and has been a prominent area in compiler research. It has been shown that context-sensitive heap analyses are more precise than the insensitive ones, but their scalability continues to be a cause of concern. Though the value-contexts approach improves the scalability of classical call-string based context-sensitive analyses, it still does not scale well for several popular whole-program heap analyses. In this talk, I will discuss our three-staged approach that lets us scale complex whole-program value-contexts based heap analyses for large programs. The ideas involved are based on an important observation that we do not need to compare the complete value-contexts at each call-site. Apart from this, I will also give an overview of another work, named PYE (for "precise-yet-efficient”). PYE helps obtain precise results during just-in-time compilation efficiently, by splitting the work between the static Java compiler and the C2 compiler of the HotSpot JVM.

Bio

Manas is a PhD student under V. Krishna Nandivada at PACE Lab, IIT Madras. His current research focuses on balancing the precision-scalability tradeoffs while analyzing programs written in object-oriented languages. He has worked with the Soot optimization framework for static analyses, and the HotSpot JVM for just-in-time analyses. Apart from work, he writes articles on GitHub and Medium (technical and otherwise!), and experiments with Vim plugins when feeling geeky.

Abstract

For ARMv8 and RISC-V, there are concurrency models in two styles, extensionally equivalent: axiomatic models, expressing the concurrency semantics in terms of global properties of complete executions; and operational models, that compute incrementally. The latter are in an abstract micro-architectural style: they execute each instruction in multiple steps, out-of-order and with explicit branch speculation. This similarity to hardware implementations has been important in developing the models and in establishing confidence, but involves complexity that, for programming and model-checking, one would prefer to avoid. In this talk, I will present new more abstract operational models for ARMv8 and RISC-V, and an exploration tool based on them. The models compute the allowed concurrency behaviours incrementally based on thread-local conditions and are significantly simpler than the existing operational models: executing instructions in a single step and (with the exception of early writes) in program order, and without branch speculation. We prove the models equivalent to the existing ARMv8 and RISC-V axiomatic models in Coq. The exploration tool is the first such tool for ARMv8 and RISC-V fast enough for exhaustively checking the concurrency behaviour of a number of interesting examples. We demonstrate using the tool for checking several standard concurrent datastructure and lock implementations such as Michael-Scott queue and Chase-Lev deque, compiled from C++ and Rust with GCC or Clang -O3, and for interactively stepping through model-allowed executions for debugging.

Bio

Chung-Kil Hur is an associate professor in Department of Computer Science and Engineering at Seoul National University. Previously he worked as a postdoctoral researcher at Microsoft Research Cambridge,Max Planck Institute for Software Systems (MPI-SWS) and Laboratoire PPS. He obtained a Ph.D. from University of Cambridge and a B.Sc. in both Computer Science and Mathematics from Korea Advanced Institute of Science and Technology (KAIST). His current research interests are in semantics of low-level languages such as C,Rust,LLVM IR, relaxed memory concurrency, formal verification of software such as compiler and OS, and interactive theorem provers such as Coq.

Abstract

Identifying relationships among program elements is useful for program understanding, debugging, and analysis. One such kind of relationship is synonymy. Function synonyms are functions that play a similar role in code; examples include functions that perform initialization for different device drivers, and functions that implement different symmetric-key encryption schemes. Function synonyms are not necessarily semantically equivalent and can be syntactically dissimilar; consequently, approaches for identifying code clones or functional equivalence cannot be used to identify them. In this talk I will present our recent work Func2vec, a technique that learns an embedding that maps each function to a vector in a continuous vector space such that vectors for function synonyms are in close proximity. We compute the function embedding by training a neural network on sentences generated using random walks over the interprocedural control-flow graph. We show the effectiveness of Func2vec at identifying function synonyms in the Linux kernel, and its applicability to the problem of mining error-handling specifications in Linux file systems and drivers.

Bio

Cindy Rubio-Gonzalez is an Assistant Professor of Computer Science at the University of California, Davis. Prior to that position, she was a Postdoctoral Researcher in the EECS Department at the University of California, Berkeley. She received her Ph.D. in Computer Science from the University of Wisconsin--Madison in 2012. Cindy's work spans the areas of Programming Languages and Software Engineering, with a focus on program analysis for automated bug finding, program optimization,and software reproducibility. She is particularly interested in the reliability and performance of systems software and scientific computing applications. She currently leads the BugSwarm project,which collects and automatically reproduces thousands of real-world bugs from public software repositories. Among other awards, Cindy is a recipient of an NSF CAREER award 2018, Hellman Fellowship 2017, and UC Davis CAMPOS Faculty Award 2014.

Abstract

Until now, CPU designers and computer scientists have assumed Vegas rules at the hardware level: what happens in speculation stays in speculation. Yet in the wake of the Spectre and Meltdown attacks, it has become clear a new, massive class of security vulnerabilities awaits us at the microarchitectural level because this assumption is simply false. As language designers and implementors familiar with building towers of abstractions, we have assumed that virtualization through emulation made the worlds below the Turing machine undetectable, hidden behind a mathematically perfect mirror. This talk will explore how we have now learned to see through that mirror, into the very bizarre and alien world of microarchitectures, illuminating a tiny world of astounding complexity that holds profound implications for language security.

Bio

Ben L. Titzer leads Google's WebAssembly team in Munich. Before starting the WebAssembly project with Luke Wagner from Mozilla, he led the team that built the TurboFan optimizing compiler which now powers JavaScript and WebAssembly in V8. He graduated with a BS from Purdue University in 2002 and MS and PhD from UCLA in 2004 and 2007. His interests include safe programming languages for systems programming, compilers, virtual machines, nature and playing guitar.

Abstract

Modern service-oriented applications forgo semantically rich protocols and middleware when composing services. Instead, they embrace the loosely-coupled development and deployment of services that communicate via simple network protocols. Even though these applications do expose interfaces that are higher-order in spirit, the simplicity of the network protocols forces them to rely on brittle low-level encodings. To bridge the apparent semantic gap, programmers introduce ad-hoc and error-prone defensive code. Inspired by Design by Contract, we choose a different route to bridge this gap. We introduce Whip, a contract system for modern services. Whip (i) provides programmers with a higher-order contract language tailored to the needs of modern services; and (ii) monitors services at run time to detect services that do not live up to their advertised interfaces. Contract monitoring is local to a service. Services are treated as black boxes, allowing heterogeneous implementation languages without modification to services' code. Thus, Whip does not disturb the loosely coupled nature of modern services.

Bio

I'm currently a Software Engineer at Facebook thinking about privacy in the data warehouse. Previously I helped build TiVo's Targeted Audience Delivery platform. And before that I worked on the backend for Timeful (acquired by Google) and on Hulu's analytics platform. In graduate school I worked on Whip, a contract monitor for microservices. I received a Ph.D. and Master's degree from Harvard under the guidance of Stephen Chong and a Bachelor's degree from Cornell.

Abstract

Software tools imply a story. New stories can help invent new tools. Explicit stories are a great meta-tool... and provide a systematic way to improve the design of programming language design. To illustrate my point, I will present a series of pairs of stories, each time approaching a same topic with very different consequences. Many of my stories will be familiar to most of you, some other stories less so. Hopefully they will be enlightening as well as entertaining... and so will the meta-story be. (Earlier versions of this talk were given at International Lisp Conference 2009, Lisp NYC 2014-10, and more recently LambdaConf 2017.)

Bio

François-René Rideau is Co-Founder and Chief Architect at Alacris.io,a company developing a "Blockchain Layer 2 Operating System" using formal methods. Once founder of the TUNES Project, he left academic research on Programming Languages and Distributed Systems to build industrial software at ITA Software, then Google and Bridgewater Associates, before he finally became his own Entrepreneur. Long-standing member of the Lisp community, he runs the Boston Lisp Meeting and still co-maintains ASDF (the Common Lisp build system),though he recently jumped ship to Gerbil Scheme, and now uses OCaml at work.

Abstract

Goblins is an actor model implementation for Racket, inspired by object-capability secure distributed progamming languages such as E with a few new twists of its own thrown in. Goblins is the foundation for a project to build secure, player/user-programmable virtual worlds named Spritely. Spritely builds on the success of the ideas from the ActivityPub protocol, which has succeeded at connecting together various distributed social network projects, extending them to pull in rich interactive and game aspects. See what is built so far, what's coming next, and how the actor model and object capabilities tie together all these ideas to enable a robust and secure distributed system.

Bio

Christopher Lemmer Webber is a user freedom advocate who works on distributed social network technology. They are most well known for their work on the W3C ActivityPub federated protocol, which connects over 1.5 million users across the distributed social web. Chris enjoys programming in lisp/scheme flavored languages, especially Racket.

Abstract

Programming languages differ a lot in the ways of their development. Who is responsible for including new features and removing obsolete ones? What is a process of doing that? Is there any more or less formal language definition? How respected is that definition by the compilers? How is this changed if there is only one compiler? These questions are answered quite differently for various programming languages. Haskell takes its own way which was crystallized relatively recently. We have GHC Proposals in the form of the GitHub pull requests to suggest new language features. We also have the GHC Steering Committee with its own process to either accept or reject proposals. Finally, there is the Haskell 2020 Language (Haskell Prime) Committee with its own problem of being effectively dead. In this talk, I’d like to describe how this system works for Haskell and why it doesn’t work sometimes. I will discuss the most recent examples of transforming Haskell kinds system towards dependent types (started before the GHC Steering Committee was formed) and introducing linear types in Haskell (subject to the full-blown Committee discussion) among others. I will also characterize the current state and perspectives of the Haskell standardization process.

Bio

Vitaly Bragilevsky serves as both the Haskell 2020 Language Committee and the GHC Steering Committee member. He works as a Senior Lecturer at the Southern Federal University in Rostov-on-Don, Russia where he teaches undergraduate students functional programming and theory of computations. He is currently a grantee of the Fulbright Faculty Development Program holding temporary Courtesy Research Assistant position at the University of Oregon under the supervision of Prof. Zena Ariola. Vitaly Bragilevsky translated into Russian and edited translations of the several books on Haskell and the theory of programming languages. He is the author of ‘Haskell in Depth’ (Manning Publications, available via Manning’s early access program).

Abstract

The Expression Problem (EP) describes a common software issue that appears regardless of programming language or programming paradigm. As coined by Philip Wadler in 1998, EP is a new name for an old problem. Given a data type defined by cases, consider two independent extensions, namely, adding new cases to the data type and new functions over the datatype. Dozens of papers in the research literature present their solutions to the EP, using a variety of programming languages and approaches. In this talk I present a Scala-based framework that can synthesize full solutions to the EP problem. The first step is to model the domain of interest (such as mathematical expressions or geometric shapes) to ensure that our synthesized code can work with any desired application domain. Second, we demonstrate how to synthesize code that reproduces a dozen EP solutions from the literature, in C++, Java and Haskell. We designed a common API for all approaches, independent of programming language. While we do not introduce any new approach for EP, we explore the challenges that EP solutions face with binary methods and producer methods. An important contribution of this work is the scientific reproduction of known solutions from the literature. This work is based on an ongoing collaboration with with Jan Bessai (TU Dortmund) and Boris Düdder (Univ. Copenhagen).

Bio

George T. Heineman is an Associate Professor of Computer Science at WPI. His research interests are in Software Engineering, specifically synthesizing software systems from composable units. He is co-author of "Algorithms in a Nutshell (2ed)," published by O'Reilly Media in 2016. Aside from his professional pursuits, George is an avid puzzler. He invented Sujiken(R), a Sudoku variation played on a right-triangle arrangement of cells in which numbers cannot repeat in a horizontal row, vertical column or diagonal in any direction.

Abstract

We build intricate systems with complex algorithms and invariants, aiming for guarantees of correctness and performance... and then we maintain and deploy these systems with shell scripts! What *are* shell scripts? If the POSIX shell is a programming language, what are its syntax and semantics? Can we apply PL tools to reason about the shell? Why haven't prior PL attempts at understanding the shell redeemed it?

Bio

Michael Greenberg is an assistant professor at Pomona College. He received his BA in Computer Science and Egyptology from Brown University (2007) and his PhD in Computer Science from the University of Pennsylvania (2013). His work has ranged from functional-reactive JavaScript (with Shriram Krishnamurthi at Brown) to runtime verification of higher-order programs using contracts (with Benjamin Pierce at Penn) to software-defined networking (with Dave Walker at Princeton) to present activities focused on Kleene algebra with tests and the POSIX shell. He is always looking for new climbing partners.

Abstract

The Parfait static code analysis tool focuses on detecting vulnerabilities that matter in C, C++, Java and PL/SQL languages. Its focus has been on key items expected out of a commercial tool that lives in a commercial organization, namely, precision of results (i.e., high true positive rate), scalability (i.e., being able to run quickly over millions of lines of code), incremental analysis (i.e., being able to run over deltas of the code quickly), and usability (i.e., ease of integration into standard build processes, reporting of traces to the vulnerable location, etc). Today, Parfait is used by thousands of developers at Oracle worldwide on a day-to-day basis. In this presentation we’ll sample a flavour of Parfait — we explore some real world challenges faced in the creation of a robust vulnerability detection tool, look into two examples of vulnerabilities that severely affected the Java platform in 2012/2013 and most machines in 2017/2018, and conclude by recounting what matters to developers for integration into today’s continuous integration and continuous delivery (CI/CD) pipelines.

Bio

Cristina is the Director of Oracle Labs Australia and an Architect at Oracle. Headquartered in Brisbane, the Lab focuses on Program Analysis as it applies to finding vulnerabilities in software and enhancing the productivity of developers worldwide. Prior to founding Oracle Labs Australia, Cristina was the Principal Investigator of the Parfait bug tracking project at Sun Microsystems, then Oracle.

Abstract

Computational effects, such as mutable state, memory allocation, non-determinism, and I/O interaction, allow programs to implicitly exhibit complex behaviour. I will discuss three abstractions that allow programmers to extend a language with user-defined effects. Control operators (call-cc, shift/reset, etc.) are a well-established abstraction for user-defined effects, and I will consider the specific shift/dollar without answer-type-modification. Since the 90s, functional languages have used monads for user-defined effects, and I will consider Filinski's monadic reflection. In the last decade, the community started looking into Plotkin and Pretnar's handlers for algebraic effects (extensible effects in Haskell, OCaml multicore, the Eff, Frank, Koka, and Links programming languages) as a programming abstraction, and I will consider deep handlers without forwarding. I will compare the relative expressive power of these three abstractions using Felleisen's notion of macro-translations. This comparison demonstrates the sensitivity of relative expressiveness of user-defined effects to seemingly orthogonal language features. This talk is based on the paper: On the expressive power of user-defined effects: effect handlers,monadic reflection, delimited control. Yannick Forster, Ohad Kammar, Sam Lindley, and Matija Pretnar. Proceedings of the 22nd ACM SIGPLAN International Conference on Functional Programming, PACMPL 1(ICFP): 13:1-13:29 (2017),arXiv:1610.09161, DOI: 10.1145/3110257.

Bio

Ohad Kammar is a postdoctoral research associate at the University of Oxford Department of Computer Science, a Career Development Fellow at Balliol College Oxford, and an incoming Royal Society University Research Fellow at the University of Edinburgh School of Informatics. His fields of interests include programming language modelling and design, logic, and the foundations of computer science.

Abstract

We study information flows arising from timing channels in the presence of automatic memory management. We construct a series of example attacks that illustrate how garbage collectors form a shared resource that can be used to reliably leak sensitive information at a rate of up to 1 byte/sec on a contemporary general-purpose computer. The created channel is also observable across a network connection in a datacenter-like setting. We subsequently present a design of an automatic memory management system and prove that the presented attacks are impossible on garbage collectors satisfying this design. The guarantees provided by the language has been mechanized in the Coq proof assistant.

Bio

Mathias is a third year PhD student in the Logic and Semantics group at Aarhus University in Denmark, advised by Aslan Askarov. His work is in the area of language-based security, with a focus on provable mitigation of side channels. In general, anything related to compilers and the semantics of programming languages will be on his list of interests.

Abstract

Type systems and syntactic sugar are both valuable to programmers, but sometimes at odds. While sugar is a valuable mechanism for implementing realistic languages, the expansion process obscures program source structure. As a result, type errors can reference terms the programmers did not write (and even constructs they do not know), baffling them. The language developer must also manually construct type rules for the sugars, to give a typed account of the surface language. We address these problems by presenting a process for automatically reconstructing type rules for the surface language using rules for the core. We have implemented this theory, and show several interesting case studies.

Bio

Justin Pombrio is a recent PhD graduate from Brown University. His research is mainly in programming languages, with a focus on syntactic sugar, but also includes CS education.

Abstract

The spectacular results achieved by computer science in the recent years rely on hidden, obscure, and badly specified components that lurk at the very heart of our computing infrastructure. Consider DWARF debug information. Debug information is relied upon by debuggers and plays a key role in the in the implementation of program analysis tools. More surprisingly, debug information can be relied upon by the runtime of high-level programming languages, for instance by C++ to unwind the stack and implement exceptions. The debug information itself can be pervaded by subtle bugs, making the whole infrastructure unreliable. In this talk I will describe techniques and tools to perform validation and synthesis of the DWARF stack unwinding tables. I will also report on adventurous projects that we might build on top of reliable DWARF information.

Bio

Francesco Zappa Nardelli is a Researcher at Inria Paris – Rocquencourt. His research interests focus on concurrent computation on top of relaxed memory models, ranging from hardware models of modern architectures to high-level programming language specification and compilation. He received his Ph.D. from Université Paris 7 in 2003. Since then, he has worked on language design for distributed computation, type systems for integrating typed and untyped code in scripting languages, and tool support for semantics (including the Ott tool).

Abstract

I will present Call-by-name (CBN) Gradual Type Theory [1], an axiomatic semantics of CBN gradual typing that directly axiomatizes the extensionality (eta) principles of types and the "gradual guarantee" of [2]. By axiomatizing the "term precision" relation of [2] we can provide a specification for casts as meets and joins with respect to precision. Using our type theory, we then show that the classic function contract ([3]) is in fact the unique implementation of function casts that satisfies extensionality and the gradual guarantee. This shows that the combination of these properties is a very strong constraint on the design of a gradually typed language and any implementation that departs must sacrifice one of these properties (typically extensionality).

[1] Call-by-Name Gradual Type Theory, New and Licata, to appear FSCD '18,https://arxiv.org/abs/1802.00061

[2] Refined Criteria for Gradual Typing, Siek, Vitousek, Cimini and Boyland, SNAPL '15, http://drops.dagstuhl.de/opus/volltexte/2015/5031/

[3] Contracts for Higher-Order Functions, Findler and Felleisen, ICFP '02,https://dl.acm.org/citation.cfm?id=581484

Bio

Max New is a PhD student at Northeastern University working on the semantic foundations of programming languages. He hates proving the same theorem twice and you should too.

Abstract

One of the several interesting challenges of probabilistic programming is that of compiling probabilistic programs to inference algorithms. One of these inference algorithms, Gibbs sampling, is particularly relevant because it is often statistically efficient, but unfortunately, it is difficult to derive and therefore compile to.

In this talk, after a brief explanation of probabilistic programming and why its relevance to data science, I will explain some of the ideas behind the design of a compiler from (very) simple probabilistic programs to Gibbs sampling. I will also attempt to explain what it would mean for such a compiler to be correct.

Bio

Jean-Baptiste Tristan is a researcher in the machine learning group at Oracle Labs. He holds a Ph.D. in Computer Science from the French Institute for Research in Computer Science and Automation (INRIA) and a M.Sc. in Computer Science from the Ecole Normale Superieure of Paris.

Abstract

Years ago, D started modestly as an improved offering in the realm of systems programming languages, sharing a good deal of philosophy with C and C++. With time, however, D became a very distinct language with unique features (and, of course, its own mistakes).

One angle of particular interest has been D's ability to perform compile-time introspection. Artifacts in a D program can be "looked at" during compilation. Coupled with a full-featured compile-time evaluation engine and with an ability to generate arbitrary code during compilation, this has led to a number of interesting applications.

This talk shares early experience with using these features of the D language. Design by Introspection is a proposed programming paradigm that assembles designs by performing introspection on components during compilation.

Bio

Andrei Alexandrescu is a researcher, software engineer, and author. He wrote three best-selling books on programming (Modern C++ Design, C++ Coding Standards, and The D Programming Language) and numerous articles and papers on wide-ranging topics from programming to language design to Machine Learning to Natural Language Processing. Andrei holds a PhD in Computer Science from the University of Washington and a BSc in Electrical Engineering from University "Politehnica" Bucharest. He currently works on the D Language Foundation. http://erdani.com

Abstract

I will summarize our work on a declarative programming language that offers native language support for model (or instance) finding. This capability can be used to express predictive (e.g. machine learning) and prescriptive (e.g. combinatorial optimization) analytics. The presentation gives an overview of the platform and the language. In particular, it focuses on the important role of integrity constraints,which are used not only for maintaining data integrity, but also, for the formal specification of complex optimization problems and probabilistic programming.

Bio

Mr. Molham Aref is the Chief Executive Officer of Relational AI. Mr. Aref has more than 25 years of experience in developing and implementing enterprise-grade analytic, predictive, optimization and simulation solutions for the demand chain, supply chain, and revenue management across various industries. Relational AI combines the latest advances in Artificial Intelligence with a understanding of business processes to develop solutions that shape better decisions, improve agility, and reduce risk. Prior to Relational AI, he was co-founder and CEO of LogicBlox where he led the company from inception through a successful sale to Infor. Previously, he was CEO of Optimi (acquired by Ericsson),a leader in wireless network simulation and optimization, and co-founder of Brickstream (renamed Nomi and then acquired by FLIR), a leading provider of computer-vision-based behavior intelligence solutions.

Abstract

Creativity is seen as a core cognitive ability for tasks like storytelling, conversation, and aesthetic design. Simulating creative processes with computers enables them to amplify humans' natural creativity and to support applications like interactive narrative, chatbots, and procedural level generators for games. By now, a large number of techniques exist for simulating creativity, such as search, planning, production grammars, logic programming, stochastic modeling from data, and hybrid approaches. However, even the simplest methods are commonly considered programming tasks for "AI experts." High-level programming languages have the potential to broaden participation in authorship of these systems, but only if they are designed with compositionality -- the mathematical principle that's also functional programming's secret weapon -- in mind. This talk will discuss what compositionality has achieved so far for creative computing and what we can do with it next.

Bio

Chris Martens is an assistant professor in the Computer Science (CSC) Department at NC State, where she is affiliated with the Digital Games research group. Her interests span artificial intelligence, programming languages, and formal methods.

Abstract

Gradual Typing is the idea that we can support both static and dynamic type checking in different parts of the same program in the same language. Most existing work on gradual typing aims to add gradual typing to an existing language. However, this severely constrains some key decisions in creating gradually typed languages, often leading designers to trade off either soundness or efficiency.

I will talk about designing a nominal object-oriented language with gradual typing as a core component from the start. This affects key design decisions for the other type system features, as well as the design of the runtime. Experiments with a prototype language I implemented suggest that this is a viable approach to achieve sound, yet efficient gradual typing. I will present those results and talk about some key design challenges that have yet to be solved.

Bio

Fabian Muehlboeck is a Ph.D. student working with Ross Tate at Cornell University.

Abstract

Deep learning is formulated around backpropagation, a method for computing gradients of a particular class of data flow graphs to train models to minimize error by gradient descent. Backpropagation is a special case of a more general method known as reverse mode automatic differentiation (AD) for computing gradients of functions expressed as programs. Reverse mode AD imposes only a small constant-factor overhead in operation count over the original computation, but has storage requirements that grow, in the worst case, in proportion to the time consumed by the original computation. This storage blowup can be ameliorated by check pointing, a process that reorders application of classical reverse-mode AD over an execution interval to tradeoff space vs. time. Application of check pointing in a divide-and-conquer fashion to strategically chosen nested execution intervals can break classical reverse-mode AD into stages which can reduce the worst-case growth in storage from linear to logarithmic. Doing this has been fully automated only for computations of particularly simple form, with checkpoints spanning execution intervals resulting from a limited set of program constructs. We show how the technique can be automated for arbitrary computations. Doing so relies on implementing general purpose mechanisms for counting the number of instructions executed by a program, interrupting the execution after a specified number of steps, and resuming the execution with a nonstandard interpretation. We implement these general purpose mechanisms with a compiler that converts programs to continuation-passing style (CPS). The process of efficiently computing gradients with check pointing requires running, and rerunning, little bits of the program out of order. This is made easier by applying the technique to functional programs. There is a deeper and higher-level message in this talk: machine learning can benefit from advanced techniques from programming language theory.

Bio

Jeffrey M. Siskind received the B.A. degree in Computer Science from the Technion, Israel Institute of Technology, Haifa, in 1979, the S.M. degree in Computer Science from the Massachusetts Institute of Technology (M.I.T.), Cambridge, in 1989, and the Ph.D. degree in Computer Science from M.I.T. in 1992. He did a postdoctoral fellowship at the University of Pennsylvania Institute for Research in Cognitive Science from 1992 to 1993. He was an assistant professor at the University of Toronto Department of Computer Science from 1993 to 1995, a senior lecturer at the Technion Department of Electrical Engineering in 1996, a visiting assistant professor at the University of Vermont Department of Computer Science and Electrical Engineering from 1996 to 1997, and a research scientist at NEC Research Institute, Inc. from 1997 to 2001. He joined the Purdue University School of Electrical and Computer Engineering in 2002 where he is currently an associate professor. His research interests include computer vision, robotics, artificial intelligence, neuroscience, cognitive science, computational linguistics, child language acquisition, automatic differentiation, and programming languages and compilers.

Abstract

The polymorphic blame calculus integrates static typing, including universal types, with dynamic typing. The primary challenge with this integration is preserving parametricity: even dynamically-typed code should satisfy it once it has been cast to a universal type. Ahmed et al. (2011) employ runtime type generation in the polymorphic blame calculus to preserve parametricity, but a proof that it does so has been elusive. Matthews and Ahmed (2008) gave a proof of parametricity for a closely related system that combines ML and Scheme, but later found a flaw in their proof. In this work we prove that the polymorphic blame calculus satisfies relational parametricity. The proof relies on a step-indexed Kripke logical relation. The step-indexing is required to make the logical relation well-defined in the case for the dynamic type. The possible worlds include the mapping of generated type names to their concrete types and the mapping of type names to relations. We prove the Fundamental Property of this logical relation and that it is sound with respect to contextual equivalence.

Bio

Dustin Jamner is an undergraduate at Northeastern University in Computer Science, working with Amal Ahmed.

Abstract

Contract-based software development is a leading methodology for the construction of safety- and mission-critical embedded systems. Contracts are an effective way to establish boundaries between components and can be used efficiently to verify global properties by using compositional reasoning techniques. A contract specifies the assumptions a component makes on its context and the guarantees it provides. Requirements in the specification of a component are often case-based, with each case describing what the component should do depending on a specific situation (or mode) the component is in.

This talk introduces CoCoSpec, a mode-aware assume-guarantee-based contract language for embedded systems. CoCoSpec is built as an extension of the Lustre language and lets users specify mode behavior directly, instead of encoding it as conditional guarantees, thus preventing a loss of mode-specific information. Mode-aware model checkers supporting CoCoSpec can increase the effectiveness of the compositional analysis techniques found in assume-guarantee frameworks and improve scalability. Such tools can also produce much better feedback during the verification process, as well as valuable qualitative information on the contract itself. I will presents the CoCoSpec language and illustrate the benefits of mode-aware model-checking on a case study involving a flight-critical avionics system. The evaluation uses Kind 2, a collaborative, parallel, SMT-based model checker developed at the University of Iowa that provides full support for CoCoSpec.

Bio

Cesare Tinelli received a Ph.D. in Computer Science from the UIUC in 1999 and is currently a professor in Computer Science at the University of Iowa. His research interests include automated reasoning and formal methods. He has done seminal work in automated reasoning, in particular in Satisfiability Modulo Theories (SMT), a field he helped establish through his research and service activities. His work has been funded both by governmental agencies (AFOSR, AFRL, DARPA, NASA, and NSF) and corporations (Intel, General Electric, Rockwell Collins, and United Technologies) and has appeared in more than 70 refereed publications. He is a founder and coordinator of the SMT-LIB initiative, an international effort aimed at standardizing benchmarks and I/O formats for SMT solvers. He has led the development of the award winning Darwin theorem prover and the Kind model checker. He co-leads the development of CVC4,a widely used and award winning SMT solver, and StarExec, a cross community web-based service for the comparative evaluation of logic solvers.

Abstract

Programming language research keeps inventing more powerful dynamic and static features for programming language. Ideally, thorough language design should be able to propose a single, unified, powerful yet simple view of them as orthogonal aspects of a single programming language that would remain usable by everyone. In practice, it always takes much more time and effort to find the simple/orthogonal presentation than to come up with the desirable features, and we become emotionally attached to programming language monoliths (C++, Scala, GHC Haskell, OCaml, Common Lisp...) that suffer from feature creep. An alternative is to design programming systems as multi-language environment instead. This point of view makes it easier to detect flaws in a proposed programming system design: there is no clear objective definition of what "orthogonal features" really mean, but we have clear ideas of how multi-language systems should work. The Racket Manifesto says: "Each language and component must be able to protect its specific invariants".

In this work we give a formal specification, in terms of full abstraction, for what it means for a new language added to the system to not introduce "abstraction leaks" in the other languages, and discuss scenarios where this translates in (informal) usability properties: having a nice teachable language subset, or gracefully introducing an advanced language for experts. We present a multi-language design that exhibits this formal property by adding a language L with linear types and linear state to a typical ML-style programming language U. L allows to write programs with resource usage guarantees (space consumption, typestate-style safety guarantees) that were not possible in U, without introducing abstraction leaks. We will demonstrate several useful examples, some of them that crucially rely on the fine-grained use of language boundaries, at a subterm level rather than whole-module level.

Bio

Abstract

We present FLIX, a declarative programming language for specifying and solving least fixed point problems, particularly static program analyses. FLIX is inspired by Datalog and extends it with lattices and monotone functions. Using FLIX,implementors of static analyses can express a broader range of analyses than is currently possible in pure Datalog, while retaining its familiar rule-based syntax. We define a model-theoretic semantics of FLIX as a natural extension of the Datalog semantics. This semantics captures the declarative meaning of FLIX programs without imposing any specific evaluation strategy. An efficient strategy is semi-naïve evaluation which we adapt for FLIX. We have implemented a compiler and runtime for FLIX, and used it to express several well-known static analyses, including the IFDS and IDE algorithms. The declarative nature of FLIX clearly exposes the similarity between these two algorithms.

This work has previously been presented at PLDI 2016. The talk will cover some of that material as well as recent developments.

Bio

The speaker is a post doctoral fellow at the University of Waterloo.

Abstract

Safety of a distributed protocol means that the protocol never reaches a bad state, e.g., a state where two nodes become leaders in a leader-election protocol. Proving safety is obviously undecidable since such protocols are run by an unbounded number of nodes, and their safety needs to be established for any number of nodes. I will describe a deductive approach for proving safety, based on the concept of universally quantified inductive invariants --- an adaptation of the mathematical concept of induction to the domain of programs. In the deductive approach, the programmer specifies a candidate inductive invariant and the system automatically checks if it is inductive. By restricting the invariants to be universally quantified, this approach can be effectively implemented with a SAT solver.

This is a joint work with Ken McMillan (MSR), Oded Padon (TAU), Aurojit Panda(Berkeley) , and Sharon Shoham(TAU) and was integrated into the IVY system: http://www.cs.tau.ac.il/~odedp/ivy/. The work is inspired by Shachar Itzhaky's thesis available from http://people.csail.mit.edu/shachari/

Bio

Mooly Sagiv is a professor in the School of Computer Sciences at Tel-Aviv University. He is a leading researcher in the area of large scale (inter-procedural) program analysis, and one of the key contributors to shape analysis. His fields of interests include programming languages, compilers, abstract interpretation, profiling, pointer analysis, shape analysis, inter-procedural dataflow analysis, program slicing, and language-based programming environments. Sagiv is a recipient of a 2013 senior ERC research grant for Verifying and Synthesizing Software Composition. Prof. Sagiv served as Member of the Advisory Board of Panaya Inc acquired by Infosys. He received best-paper awards at PLDI'11 and PLDI'12 for his work on composing concurrent data structures and a ACM SIGSOFT Retrospective Impact Paper Award (2011) for program slicing. He is an ACM fellow and a recipient of Microsoft Research Outstanding Collaborator Award 2016.

Abstract

Automating programming and debugging are long-standing goals in computer science. In spite of significant progress in formal methods over the years, we remain very far from achieving these goals. For example, a freshman CS major will typically program circles around today's best program synthesizers. Debugging and verification tools rely on formal specifications, which are hard to provide in many important applications.

Two critical components of the gap between human and machine programmers are that humans learn from experience, i.e., data, and can easily generalize from incomplete problem definitions. In this talk, I will present a general framework for formal methods, based on Bayesian statistical learning, that aims to eliminate these differences. In our framework, descriptions of programming tasks are seen to be "clues" towards a hidden (probabilistic) specification that fully defines the task. Large corpora of real-world programs are used to construct a statistical model that correlates specifications with the form and function of their implementations. The framework can be implemented in a variety of ways, but in particular, through a neural architecture called Bayesian variational encoder-decoders. Inferences made using the framework can be used to guide traditional algorithms for program synthesis and bug-finding.

I will show that this data-driven approach can lead to giant leaps in the scope and performance of automated program synthesis and debugging algorithms. Specifically, I will give a demo of Bayou, a system for Bayesian inductive synthesis of Java programs that goes significantly beyond the state of the art in program synthesis. I will also describe Salento, a debugging system based on our framework that can find subtle violations of API contracts without any kind of specification.

Bio

Swarat Chaudhuri is an Associate Professor of Computer Science at Rice University. His research lies at the interface of programming systems and artificial intelligence. Much of his recent work is on program synthesis, the problem of automatically generating computer programs from high-level specifications.

Abstract

Like many modern programming languages, C++ supports both generic programming (templates) and object-oriented programming (inheritance and virtual functions). Unlike many modern programming languages, the generic programming mechanism does not fully leverage or interact with the type system. It amounts to compile-time duck typing: C++ function templates, like functions from a non-statically typed programming language, have their full interface implicit in their implementations -- making it hard to discern what that interface actually is. The concept of Concepts (compile-time typeclasses) was slated for introduction into C++ in the C++11 version of the standard, then C++14, then C++17, now C++20... What can we do to work around their absence?

Relatedly, how do we bridge the gap between static and dynamic polymorphism? C++ provides both, and sometimes we want to mix and mash them. Template tricks can also be done to accomplish this as well.

Bio

Jimmy Hartzell is a software developer and C++ instructor at Tower Research.

Abstract

Topology, when viewed from an unusual perspective (formal topology), describes how to compute with certain objects that are beyond the reach of induction, such as real numbers, probability distributions, streams, and function spaces. Accordingly, one gets a programming language whose types are spaces and whose functions are continuous maps.

Does this language have pattern matching? In functional programming, pattern matching allows definition of a function by partitioning the input and defining the result in each case. We generalize to programming with spaces, where patterns need not represent decidable predicates and also may overlap, potentially allowing nondeterministic behavior in overlapping regions. These overlapping pattern matches are useful for writing a wide array of computer programs on spaces, such as programs that make approximate computations or decisions based on continuous values or that manipulate "partial" datatypes.

This talk will introduce topology from a computational perspective, and explore some programs that can be built with this framework using overlapping pattern matching.

Bio

Ben Sherman is a second-year PhD student at MIT, advised by Adam Chlipala and Michael Carbin.

Abstract

Earlier comparative studies of language support for generic programming (GP) have shown that mainstream object-oriented (OO) languages such as C# and Java provide weaker support for GP as compared with functional languages such as Haskell or SML.

One reason is that generic mechanisms of C# and Java are based on F-bounded polymorphism, which has certain deficiencies. Some of these deficiencies are eliminated in more recent languages, such as Scala and Kotlin. However, there are peculiarities of object-oriented languages as a whole, which prevent them from being as expressible as Haskell in terms of generic programming.

In this talk we will cover the main problems of language support for generic programming in C#/Java as well as other modern object-oriented languages, including Scala and Swift. It turns out that all of these languages follow the same approach to constraining type parameters, which leads to inevitable shortcomings. To overcome them, an alternative approach is suggested in several extensions for OO languages, in particular, JavaGenus. The advantages and drawbacks of both approaches will be discussed in the talk.

Here are Julia's Slides.

Bio

Julia Belyakova graduated in 2014 from Southern Federal University (Rostov-on-Don, Russia) as Master of Science in “Computer Science and Information Technologies”. She is enrolled for PhD program in Southern Federal University and came to Northeastern University as a visiting scientist. She is currently doing research in the area of generic programming for object-oriented languages.

Abstract

Billions of people use browsers to access the web from a variety of devices, but it remains difficult to write and debug the client-side of web applications. The browser exposes unfamiliar, redundant, and incompatible abstractions for a variety of common tasks, preventing developers from adapting existing code written for other platforms. The browser environment is completely event-driven and pervasively concurrent, resulting in complex control flow and race conditions that are difficult to debug.

In this talk, I will describe how my research makes it easier to write and debug web applications. Doppio is a JavaScript runtime system that lets developers run unaltered code written in general-purpose languages, such as Java and C/C++, directly inside the browser. Browsix further enhances Doppio with a kernel, processes, and shared operating system resources, letting multiple off-the-shelf programs concurrently interoperate on the same webpage. ReJS is a low-overhead and high-fidelity time-traveling debugger that lets developers instantaneously step forward and backward through a program's execution. ReJS accurately recreates the application's complete behavior, including the GUI and multiple types of race conditions, while producing miniscule and portable application traces. These resources transform the browser into a first-class application platform.

Bio

John Vilk is a Ph.D. candidate in the College of Information and Computer Sciences at the University of Massachusetts Amherst. He is a member of the PLASMA lab, and is advised by Emery Berger. John's research aims to improve the browser as an application platform by making it easier to write, debug, and optimize complex web applications. John is a Facebook Fellow (2015), and his work on Doppio: Breaking the Browser Language Barrier is a SIGPLAN Research Highlight (2014) and a PLDI Distinguished Artifact (2014). His research forms the basis of Microsoft ChakraCore's time-traveling debugger and lets millions of people interact with legacy software and games in their browser at the Internet Archive (https://archive.org/). John received his MS from the University of Massachusetts Amherst (2013) and his BS from Worcester Polytechnic Institute (2011).

You can learn more about John at his website, https://jvilk.com/.

Abstract

It has long been noted that research on contracts and gradual typing bears a striking resemblance to Dana Scott's work on retracts in domain theory. However, a concrete relationship has been elusive, especially since the notion of *blame* has seemingly no counterpart in domain theory.

We connect these fields by means of gradual type precision, which has been characterized previously using blame in [1]. We propose a definition in terms of section-retraction pairs, allowing us to relate blame and retractions in a precise way. Our definition agrees with [1] on most rules, but differs in a way that suggests a modification to their definition of blame.

[1] Wadler and Findler, Well-typed Programs can't Be Blamed: http://homepages.inf.ed.ac.uk/wadler/topics/blame.html#blame-esop

Bio

Max New is a PhD student at Northeastern University working on the semantic foundations of programming languages. He hates proving the same theorem twice and you should too.

Abstract

The simply-typed λ-calculus, with only function types, is strongly normalizing, and its program equivalence relation is decidable: unlike in more advanced type system with polymorphism or effects, the natural "syntactic" equivalence (βη-equivalence) corresponds to natural "semantic" equivalence (observational or contextual equivalence), and is decidable. Adding product types (pairs) is easy and preserves these properties, but sums (disjoint unions) are, surprisingly, harder. It is only in 1995 that Neil Ghani proved that the equivalence in presence of sums is decidable, and the situation in presence of the empty type (zero-ary sum) was unknown.

We propose an equivalence algorithm for sums and the empty type that takes inspiration from a proof-theoretic technique, named "focusing",designed for proof search. The exposition will be introductory; I will present the difficulties caused by sums and the empty type, present some of the existing approaches for sums in the literature, introduce focusing, and give a high-level intuition of our saturation-based algorithm and its termination argument. No previous knowledge of focusing is assumed. I expect some familiarity with typed lambda-calculi, but will recall all concepts used (for example, βη-equivalence) during the talk.

Bio

Gabriel is interested in theoretical aspects of type systems, programming language implementation, general programming language concepts, and even some syntactic aspects. He has a preference for the formalizable aspects, or formalizable approaches to programming language aspects, rather than the often subjective appeal to taste or intuition.

Abstract

Hybrid apps help developers build multiple apps for different platforms with less duplicated effort, by providing platform-specific functionality via native code and user interactions via javascript code. however, most hybrid apps are developed in multiple programming languages with different semantics, complicating programming. moreover, untrusted javascript code may access device-specific features via native code, exposing hybrid apps to attacks. Unfortunately, there are no existing tools to detect such vulnerabilities. In this paper, we present HybriDroid, the first static analysis framework for Android hybrid apps. First, we investigate the semantics of interoperation of Android Java and JavaScript. Then, we design and implement a static analysis framework that analyzes inter-communication between Android Java and JavaScript. We demonstrate HybriDroid with a bug detector that identifies programmer errors due to the hybrid semantics, and a taint analyzer that finds information leaks cross language boundaries. Our empirical evaluation shows that the tools are practically usable in that they found previously uncovered bugs in real-world Android hybrid apps and possible information leaks via a widely-used advertising platform.

The bulk of this presentation will focus on ASE 2016 work on analysis of hybrid apps (1), a blend of per-platform native code and portable JavaScript. I will also briefly discuss two other recent projects involving WALA: ASE 2015 work on a practically tunable static analysis framework for large-scale JavaScript applications (2), and ISSTA 2015 work on scalable and precise taint analysis for Android (3).

1: Sungho Lee, Julian Dolby, Sukyoung Ryu: HybriDroid: static analysis framework for Android hybrid applications. ASE 2016: 250-261

2: Yoonseok Ko, Hongki Lee, Julian Dolby, Sukyoung Ryu: Practically Tunable Static Analysis Framework for Large-Scale JavaScript Applications (T). ASE 2015: 541-551

3: Wei Huang, Yao Dong, Ana Milanova, Julian Dolby: Scalable and precise taint analysis for Android. ISSTA 2015: 106-117

Bio

Julian Dolby is a Research Staff Member at the IBM Thomas J. Watson Research Center, where he works on a range of topics, including static program analysis, software testing, concurrent programming models and the semantic Web. He is one of the primary authors of the publically available Watson Libraries for Analysis (WALA) program analysis infrastructure, and his recent WALA work has focused on creating the WALA Mobile infrastructure.

His program analysis work has recently focused on scripting languages like JavaScript and on security analysis of Web applications; this work has been included in IBM products, most notably Rational AppScan, Standard Edition and Source Edition. He was educated at the University of Illinois at Urbana-Champaign as a graduate student where he worked with Professor Andrew Chien on programming systems for massively-parallel machines.

Abstract

We will present and demonstrate the usefulness of the sequent calculus as a formal model of computation based on interactions between producers and consumers of results. This model leads to a better understanding of call-by-name evaluation by reconciling the conflicting principles of extensionality and weak-head evaluation, thus internalizing a known parametricity result. It allows one to explore two dualities of computation: the duality between call-by-name and call-by-value, and the duality between construction and deconstruction. This ultimately leads to a better linguistic foundation for co-induction as dual to induction. From a more practical point of view, the sequent calculus provides a useful inspiration for the design of intermediate languages.

Bio

TBD

Abstract

In this talk I introduce a probabilistic programming language with continuous random variables and soft constraints, define contextual equivalence for the language, and then present a logical relation that is sound with respect to contextual equivalence.

The question of equivalence for probabilistic programs is interesting and difficult because the meaning of a program is a measure defined by integrating over the space of all of the program's possible evaluations. Thus creating a workable logical relation requires proving integral equalities involving intermediate measures that are ``equal enough'' but still respect contextual equivalence.

Bio

Ryan Culpepper is a research scientist at Northeastern University. He works on probabilistic programming as well as extensible languages and tools for building domain-specific languages. He received his PhD at Northeastern and did a postdoc at the University of Utah.

Abstract

Consistency is hard and coordination is expensive. As we move into the world of connected 'Internet of Things' style applications, or large-scale mobile applications, devices have less power, periods of limited connectivity, and operate over unreliable asynchronous networks. This poses a problem with shared state: how do we handle concurrent operations over shared state, while clients are offline, and ensure that values converge to a desirable result without making the system unavailable?

We look at a new programming model, called Lasp. This programming model combines distributed convergent data structures with a dataflow execution model designed for distribution over large-scale applications. This model supports arbitrary placement of processing node: this enables the user to author applications that can be distributed across data centers and pushed to the edge.

Bio

Christopher Meiklejohn loves distributed systems and programming languages. Previously, Christopher worked at Basho Technologies, Inc. on the distributed key-value store, Riak. Christopher develops a programming model for distributed computation, called Lasp. Christopher is currently a Ph.D. student at the Université Catholique de Louvain in Belgium.

Abstract

The Hack programming language developed at Facebook is an evolution of PHP. With tens of millions of lines of PHP code in house, the lack of type and poor IDE support were felt to be major threats to Facebook. We designed Hack gradually, adding feature at regular intervals and encouraging developers to adopt them in their code. This talk will touch on the design of the Hack language, its type system, the implementation of the type checker, and the social processes involved in converting 20 millions lines of untyped code to a rich type system.

HHVM is an open source virtual machine for PHP and Hack programs. It was developed by Facebook but is also used by Wikipedia, Baidu, Etsy, and many others. This talk will discuss the history of the project, what goes on behind the scenes while executing your PHP code, and what we're currently working on.

Bio

Julien Verlaguet is a OCaml programmer with a Master from Université Pierre et Marie Curie in Paris. Before joining Facebook in 2011, he worked at Esterel technologies on verified compilation. At Facebook he designed the Hack programming language, a typed offspring of PHP and managed to convince the majority of developers in the company to add types to their code.

Brett Simmers is a Software Engineer at Facebook, where he's been working on HHVM for the past five years. He primarily works on the internals of the JIT compiler and HHVM's profile-guided optimizations. Before joining Facebook he spent two years at VMware working on the Linux version of VMware Workstation.

Abstract

Static program analysis is a powerful technique for maintaining software, with applications such as compiler optimizations, code refactoring, and bug finding. Static analyzers are typically implemented in general-purpose programming languages, such as C++ and Java; however, these analyzers are complex and often difficult to understand and maintain. An alternate approach is to use Datalog, a declarative language. Implementors can express analysis constraints declaratively, which makes it easier to understand and ensure correctness of the analysis. Furthermore, the declarative nature of the analysis allows multiple, independent analyses to be easily combined.

Flix is a programming language for static analysis, consisting of a logic language and a functional language. The logic language is inspired by Datalog, but supports user-defined lattices. The functional language allows implementors to write functions, something which is not supported in Datalog. These two extensions, user-defined lattices and functions, allow Flix to support analyses that cannot be expressed by Datalog, such as a constant propagation analysis. Datalog is limited to constraints on relations, and although it can simulate finite lattices, it cannot express lattices over an infinite domain. Finally, another advantage of Flix is that it supports interoperability with existing tools written in general-purpose programming languages.

This talk provides an overview of the implementation and evaluation of the Flix functional language. The implementation involves abstract syntax tree transformations, an interpreter back-end, and a code generator back-end, and must support a number of interesting language features, such as pattern matching, first-class functions, and interoperability. The evaluation compares the interpreter and code generator back-ends in terms of correctness and performance.

Bio

Ming-Ho Yee is a Ph.D. student at Northeastern University. He works on programming language design and implementation with Jan Vitek. He received a Bachelor of Software Engineering from the University of Waterloo, and then continued for a Master of Mathematics in Computer Science under the supervision of Ondřej Lhoták.

Abstract

The precise semantics of floating-point arithmetic programs depends on the execution platform, including the compiler and the target hardware. Such platform dependencies infringe on the highly desirable goal of software portability (which is in fact promised by heterogeneous computing frameworks like OpenCL): the same program run on the same inputs on different platforms can produce different results. In other words, portability does not guarantee reproducibility, and this is a more or less accepted state of affairs.

Serious doubts on the portability of numeric applications arise when differences in results are behavioral, i.e. when they lead to changes in the control flow of a program. In this talk I will first present an algorithm that takes a numeric procedure and determines whether a given input can lead to different decisions depending merely on how the arithmetic in the procedure is compiled and executed. I will then show how this algorithm can be used in static and dynamic analyses of programs, to estimate their numeric stability. I will illustrate the results on examples characteristic of numeric computing where control flow divergence actually occurs across different execution platforms.

Joint with Yijia Gu, Mahsa Bayati, and Miriam Leeser, Northeastern University, Boston, USA

Bio

Thomas Wahl joined the faculty of Northeastern University in 2011. His research concerns the reliability (whatever that means) of complex computing systems. Two domains notorious for their fragility are concurrency and numerical computing. With colleagues, Wahl has investigated how floating-point arithmetic can "hijack" a program's computation when run on non-standard architectures, such as heterogeneous and custom-made embedded platforms. You will witness some hijacking attempts in the talk today.

Abstract

Symbolic execution, which was introduced more than four decades ago, is typically used in software testing to explore as many different program paths as possible in a given amount of time, and for each path to generate a set of concrete input values exercising it, and check for the presence of various kinds of errors including assertion violations, uncaught exceptions, security vulnerabilities, and memory corruption. A key limitation of classical symbolic execution is that it cannot generate useful test inputs if the program under test uses complex operations such as pointer manipulations and non-linear arithmetic operations.

Our research on Concolic Testing (also known as DART: Directed Automated Random Testing or Dynamic Symbolic Execution) alleviated the limitations of classical symbolic execution by combining concrete execution and symbolic execution. We demonstrated that concolic testing is an effective technique for generating high-coverage test suites and for finding deep errors in complex software applications. The success of concolic testing in scalably and exhaustively testing real-world software was a major milestone in the ad hoc world of software testing and has inspired the development of several industrial and academic automated testing and security tools.

One of the key challenges of concolic testing is the huge number of programs paths in all but the smallest programs, which is usually exponential in the number of static branches in the code. In this talk I will describe MultiSE, a new technique for merging states incrementally during symbolic execution, without using auxiliary variables. The key idea of MultiSE is based on an alternative representation of the state, where we map each variable, including the program counter, to a set of guarded symbolic expressions called a value summary. MultiSE has several advantages over conventional symbolic execution and state merging techniques: 1) value summaries enable sharing of symbolic expressions and path constraints along multiple paths, 2) value-summaries avoid redundant execution, 3) MultiSE does not introduce auxiliary symbolic values, which enables it to make progress even when merging values not supported by the constraint solver, such as floating point or function values. We have implemented MultiSE for JavaScript programs in a publicly available open-source tool. Our evaluation of MultiSE on several programs shows that MultiSE can run significantly faster than traditional symbolic execution.

Bio

Koushik Sen is an associate professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research interest lies in Software Engineering, Programming Languages, and Formal methods. He is interested in developing software tools and methodologies that improve programmer productivity and software quality. He is best known for his work on ³DART: Directed Automated Random Testing² and concolic testing. He has received a NSF CAREER Award in 2008, a Haifa Verification Conference (HVC) Award in 2009, a IFIP TC2 Manfred Paul Award for Excellence in Software: Theory and Practice in 2010, a Sloan Foundation Fellowship in 2011, a Professor R. Narasimhan Lecture Award in 2014, and an Okawa Foundation Research Grant in 2015. He has won several ACM SIGSOFT Distinguished Paper Awards. He received the C.L. and Jane W-S. Liu Award in 2004, the C. W. Gear Outstanding Graduate Award in 2005, and the David J. Kuck Outstanding Ph.D. Thesis Award in 2007, and a Distinguished Alumni Educator Award in 2014 from the UIUC Department of Computer Science. He holds a B.Tech from Indian Institute of Technology, Kanpur, and M.S. and Ph.D. in CS from University of Illinois at Urbana-Champaign.

Abstract

Aliasing is a known source of challenges in the context of imperative object-oriented languages, which have led to important advances in type systems for aliasing control. However, their large-scale adoption has turned out to be a surprisingly difficult challenge. While new language designs show promise, they do not address the need of aliasing control in existing languages.

This paper presents a new approach to isolation and uniqueness in an existing, widely-used language, Scala. The approach is unique in the way it addresses some of the most important obstacles to the adoption of type system extensions for aliasing control. First, adaptation of existing code requires only a minimal set of annotations. Only a single bit of information is required per class. Surprisingly, the paper shows that this information can be provided by the object-capability discipline, widely-used in program security. We formalize our approach as a type system and prove key soundness theorems. The type system is implemented for the full Scala language, providing, for the first time, a sound integration with Scala’s local type inference. Finally, we empirically evaluate the conformity of existing Scala open-source code on a corpus of over 75,000 LOC.

Bio

Philipp Haller is an assistant professor in the theoretical computer science group at KTH Royal Institute of Technology, the leading technical university in Sweden. His main research interests are programming languages, type systems, concurrent, and distributed programming. Philipp is co-author of Scala's async/await extension for asynchronous computations, and one of the lead designers of Scala's futures and promises library. As main author of the book "Actors in Scala," he created Scala's first widely-used actors library. Philipp was co-chair of the 2013 and 2014 editions of the Scala Workshop, and co-chair of the 2015 ACM SIGPLAN Scala Symposium. Previously, he has held positions at Typesafe, Stanford University, and EPFL. He received a PhD in computer science from EPFL in 2010.

Abstract

Julia, like R, is a dynamic language for scientific computing but, unlike R, it was explicitly designed to deliver performance competitive to traditional batch-compiled languages. To achieve this Julia's designers made a number of unusual choices, including the presence of a set of type annotations that are used for dispatching methods and speed up code, but not for type-checking. The result is that many Julia programs are competitive with equivalent programs written in C. This talk gives a brief overview of the key points of Julia's design and considers whether similar ideas could be adopted in R.

Bio

Jan Vitek, "http://janvitek.org/", is a Professor at Northeastern University CCIS. He works on the design and implementation of programming abstractions for areas and in languages that are *terrifying*. For instance, he has worked on a real-time JVM for running Java on an actual embedding real-time system, on understanding JavaScript to make it more secure, and on getting R (yes R) to be scalable for data analysis. Apparently he does these things because they are used in the "real world" to "solve problems". He also has an excellent sense of humor and didn't give me a bio, so I took some liberties and hope he doesn't mind.

Abstract

A "type refinement system" is a type system built on top of a typed programming language, as an extra layer of typing. Over the past few years Paul-André Melliès and I have investigated a categorical foundation for type refinement systems, which is based on the idea of looking at a type refinement system "backwards", in terms of the functor which erases the extra layer of types. The talk will give an introduction to this framework as well as to some of the insights that it offers, such as:

* The idea of viewing Hoare logic as a type refinement system

* The importance of the bifibrational operations of "pushing forward" and "pulling back" (which generalize strongest postconditions and weakest preconditions)

* How those operations can be combined with a monoidal closed structure, to model type refinement systems built over pure lambda calculus

I will not assume any prior background in category theory, although as background reading audience members may be interested in the lecture notes for my recent course at OPLSS (http://noamz.org/oplss16/refinements-notes.pdf, especially Ch.3).

Bio

Since receiving his PhD from Carnegie Mellon in 2009, Noam Zeilberger has been actively pursuing a career as an itinerant postdoc, with gigs in Paris (Laboratoire PPS), Madrid (IMDEA Software), Princeton (IAS), and back in Paris (MSR-Inria). He is currently a member of Inria Team Parsifal at Ecole Polytechnique. In his free time, he enjoys enumerating lambda terms, and his favorite combinators are B, C and I.

Abstract

A modern web page contains megabytes of HTML, CSS, images, and JavaScript. Loading such a page requires a browser to evaluate a complex dependency graph involving those resources; once the page is loaded, subsequent interactions between those resources and the user can lead to tricky-to-diagnose bugs. In this talk, I'll describe how tracking fine-grained data flows can allow us to reduce page load times by prioritizing the loads of the highest ancestors in the data flow graph. I'll also describe initial work in using data flows to assist with time-travel debugging (in which developers use a logging-and-replay framework to analyze buggy program executions).

Bio

James Mickens is an IEEE Knight of the Republic, an ACM Templar for Non-Open Access, and a Royal Proceeding of Her Majesty’s Royal Proceedings. His appreciation for syntactically correct code has led him to be called “a semicolon in human form.” His online shopping habits have too many dimensions to be k-means clustered, so he is only shown ads about dinosaurs and ancient siege machines. This does not bother James Mickens, and explains why he spends his summers attacking France with triceratops horns.

Abstract

This talk reports on some cool ideas I learned during a week-long seminar in Dagstuhl last month; it will present other people's work, not my own. It is aimed at an audience interested in programming language design in general, but not familiar with the theoretical treatment of pure languages and side-effects. We shall start with *demos* first, explaining short code examples in three programming languages: ML with exceptions as a warmup, then the new language 'Eff' of Matija Pretnar and Andrej Bauer, that first implemented so-called effect handlers, and finally the language 'Frank' of Conor McBride, that integrates these handlers in a more uniform style of "effectful call-by-value programming". Only second will we discuss some *theory*, in an accessible way: monads and algebraic effects, which are two distinct ways to formalize side-effects, and the difference between "direct" and "indirect" style of effectful programming. Underlying this talk are two larger, important questions of programming language design, that will be touched during the talk and we can discuss further afterwards: - Do programming language need a facility for user-defined side-effects? - When should we encode new design ideas as libraries/macros in an expressive language, and when should we design languages afresh for them?

Bio

Gabriel is interested in theoretical aspects of type systems, programming language implementation, general programming language concepts, and even some syntactic aspects. He has a preference for the formalizable aspects, or formalizable approaches to programming language aspects, rather than the often subjective appeal to taste or intuition.

Abstract

We present a Coq library that lifts an abstract yet precise notion of running-time into the type of a function. Our library is based on a monad that counts abstract steps, controlled by one of the monadic operations. The monad's computational content, however, is simply that of the identity monad so programs written in our monad (that recur on the natural structure of their arguments) extract into idiomatic OCaml code. We evaluated the expressiveness of the library by proving that red-black tree insertion and search, merge sort, insertion sort, Fibonacci, iterated list insertion, BigNum addition, and Okasaki's Braun Tree algorithms all have their expected running times.

Bio

Jay McCarthy is an associate professor at UMass Lowell in the Computer Science Department. He is a member of the PLT research group and works on the Racket programming language. He is passionate about computer science education & diversity, formal verification, programming language expressiveness, and his wonderful family.

Abstract

The domain of Internet of Things (IoT) is rapidly expanding beyond research and becoming a major industrial market with such stakeholders as major manufacturers of chips and connected objects, and fast-growing operators of low-power wide-area networks. Importantly, this emerging domain is driven by applications that leverage the infrastructure to provide users with innovative, high-value services. Because of this application-centric approach, software development plays a key role to realize the full potential of IoT. In this talk, we argue that there is a continuum between orchestrating connected objects in the small and in the large, fostering a unified approach to application development. We examine the requirements for orchestrating connected objects and address them with domain-specific design concepts. We then show how to map these design concepts into dedicated programming patterns and runtime mechanisms. Our work revolves around domain-specific notations integrated into a tool-based design methodology, dedicated to develop IoT applications. We have applied our work across a spectrum of infrastructure sizes; we present examples, ranging from an automated pilot in avionics, to an assisted living platform for the home of seniors, to a parking management system in a smart city.

Bio

Charles Consel is a professor of Computer Science at Bordeaux Institute of Technology. He served on the faculty of Yale University, Oregon Graduate Institute and the University of Rennes. His research contributions cover programming languages, software engineering, operating systems, pervasive computing, and assistive computing. He leads the Phoenix group at Inria that conducts multi-disciplinary research to design, develop, deploy and assess assistive computing support. This research combines (1) Cognitive Science to study user needs and make a rigorous assessment of assistive services; (2) Sensing and actuating expertise to support the user, based on accurate and rich interactions with the environment; (3) Computer Science to support and guide the development process of the assistive services.

Abstract

The optimization component of a compiler takes a program as input and produces another, hopefully faster, program as output. At a high level, optimizers solve a search problem over a space of programs, but the traditional architecture of a compiler's optimizer does no search at all. This talk will present the STOKE project, which is exploring the use of Monte Carlo search methods as the basis of a modern compiler optimizer. We will show that search-based program optimization can consistently improve, sometimes substantially, on current production compilers at their highest levels of optimization and can even compete with expert, hand-written assembly. We will also discuss the unique and challenging verification problems that arise when a compiler produces code using a random process.

Bio

Alex Aiken is the Alcatel-Lucent Professor and the current Tencent Chair of the Computer Science Department at Stanford. Alex received his Bachelors degree in Computer Science and Music from Bowling Green State University in 1983 and his Ph.D. from Cornell University in 1988. Alex was a Research Staff Member at the IBM Almaden Research Center (1988-1993) and a Professor in the EECS department at UC Berkeley (1993-2003) before joining the Stanford faculty in 2003. His research interest is in areas related to programming languages. He is an ACM Fellow, a recipient of Phi Beta Kappa's Teaching Award, and a former National Young Investigator.

Abstract

As software becomes increasingly more complex and yet more pervasive, poor understanding of software behavior compromises the quality and the integrity of software systems that we use. In this talk, I will show that automated analysis techniques can help to identify and reason about software behavior characteristics that matter to humans. After a brief overview of my current research directions, I will focus on techniques for identifying privacy violations in mobile applications, i.e., leakages of sensitive information such as user location and shopping preferences. I will present a set of solutions that rely on contextual, functional and usage-based clues for improving the accuracy of leakage detection and for distinguishing between “legitimate” and “illegitimate” information distribution patterns.

Bio

Julia Rubin is a Postdoctoral Researcher in the EECS department at MIT. Prior to that, she was a Research Staff Member and, part of the time, a manager at IBM Research in Haifa, Israel. She received her PhD in Computer Science from the University of Toronto, Canada in 2014. Julia’s research interests are in software engineering, program analysis and software security, focusing on improving the quality and the integrity of modern software systems. Her recent work in this area won an ACM Distinguished Paper Award at ASE, two Best Paper Awards, at SPLC and CSMR, and was nominated for Facebook’s Internet Defense Prize at the USENIX Security Symposium.

Abstract

Object capabilities are a technique for fine-grained privilege separation in programming languages and systems,with important applications in security. However, current formal characterisations do not fully capture capability-safety of a programming language and are not sufficient for verifying typical applications. Using state-of-the-art techniques from programming languages research, we define a logical relation for a core calculus of JavaScript that better characterises capability-safety. The relation is powerful enough to reason about typical capability patterns and supports evolvable invariants on shared data structures, capabilities with restricted authority over them and isolated components with restricted communication channels. We use a novel notion of effect parametricity for deriving properties about effects. We demonstrate that our results imply memory access bounds that have previously been used to characterise capability-safety.

Bio

Dominique is a postdoctoral researcher in the research group DistriNet, part of the Computer Science department of the Katholieke Universiteit Leuven. He holds a postdoctoral research fellowship of the Research Foundation - Flanders (FWO). He works on formalising properties of object-oriented and object-capability programming languages---specifically a property called effect parametricity--is the author of the grammar-combinators Haskell parsing library, and has added instance arguments to the programming language/proof assistant Agda. He is interested in information flow security, secure compilation,full abstraction, and functional and dependently typed programming languages.

Abstract

Emerging consumer electronics products are running the same software platforms that power smartphones. This leads to the appealing idea that a uniform programming abstraction can be used for app development for a range of devices, from wearables to smartphones. In practice, however, devices wary in their hardware capabilities and this has an impact on app development. In this talk, I will focus on ways in which JavaScript may be used and/or run differently to accommodate the different hardware capabilities across devices. Specifically, I will present a subset of JavaScript that strikes a balance between retaining the flavor of JavaScript (e.g. use of prototype inheritance, no explicit type declarations) and the possibility of ahead-of-time compilation to efficient code.

Bio

Satish Chandra obtained a PhD from the University of Wisconsin-Madison in 1997, and a B.Tech from the Indian Institute of Technology-Kanpur in 1991, both in computer science. From 1997 to 2002, he was a member of technical staff at Bell Laboratories, where his research focused on program analysis, domain-specific languages, and data-communication protocols. From 2002 to 2013, he was a research staff member at IBM Research, where his research focused on bug finding and verification, software synthesis, and test automation. He joined Samsung Electronics in 2013, where he leads the advanced programming tools research team. He is an ACM Distinguished Scientist.

Abstract

Analyzing real-world web applications is a challenging task. On top of understanding the semantics of JavaScript, it requires modeling of web documents, platform objects, and interactions between them. Most web applications load JavaScript code dynamically, which makes pure static analysis approaches inapplicable. In this talk, we present our journey to analyze JavaScript web applications in the wild using various approaches. From pure JavaScript programs to JavaScript web applications using platform-specific libraries, we explain technical challenges in analyzing each of them and how we built an open-source analysis framework for JavaScript, SAFE, that addresses the challenges incrementally. Finally, we discuss our ongoing work on analysis of Android hybrid applications.

Bio

Abstract

Multiprocessors and concurrent programming are now pervasive. Typically, they do not guarantee sequential consistency (a.k.a. interleaving semantics), which is the standard assumption by most work on semantics and verification. Instead, they employ subtle memory models, exposing unexpected relaxed behaviors arising from hardware and compiler optimizations.

In this talk, I will focus on one such model --- the release-acquire fragment of the C/C++11 memory model. I will describe its merits, and show how it can be further improved, without additional implementation costs, to: (i) forbid dubious behaviors that are not observed in any implementation; (ii) support fence instructions that restore sequential consistency; and (iii) admit an equivalent intuitive operational semantics.

The talk is based on a joint work with Nick Giannarakis and Viktor Vafeiadis, to be presented in POPL'16.

Bio

Ori Lahav is a postdoctoral researcher at MPI-SWS. He obtained his PhD from Tel Aviv University in the area of non-classical logics. His current research interests are in programming languages generally, with specific focus on memory models, concurrency, verification, and logic.

Abstract

This talk will informally describe protected module architectures (PMA), a security architecture that provides an assembly-level memory isolation mechanism. Then it will describe how to devise a secure (fully-abstract) compiler for an object-oriented language to PMA. Finally, it will present how to prove the said compiler to be secure and discuss open, related research trajectories.

Bio

Marco Patrignani did his bachelor and masters study in Bologna, then he obtained a Ph.D. in computer science from the KU Leuven, Belgium. There, with his advisors Dave Clarke and Frank Piessens, he studied secure (fully-abstract) compilation for Intel-SGX like architectures, i.e., untyped assembly languages extended with a memory isolation mechanism. Additionally, he investigated trace-based characterisation of the behaviour of those architectures. He is now a post-doc at MPI-SWS, Germany working on more secure-compilation-related topics with Deepak Garg.

Abstract

Weak memory models formalize the inconsistent behaviors that one can observe in multithreaded programs running on modern hardware. In so doing, they complicate the already-difficult task of reasoning about correctness of concurrent code. Worse, they render impotent most formal methods that have been developed to tame concurrency, which almost universally assume a strong (i.e., sequentially consistent) memory model. In response, we have developed a number of alternative reasoning techniques that are sound for programs running weak memory consistency. I will cover both program logics, such as relaxed separation logic, as well as theorems that allow reducing reasoning about well-structured weakly consistent implementations down to sequential consistency, and show how these can be applied to reason about a practical RCU implementation.

Bio

Viktor Vafeiadis is a tenure-track researcher at MPI-SWS. He received his BA (2004) and PhD (2008) from the University of Cambridge. Before joining MPI-SWS in October 2010, he was a postdoc at Microsoft Research and at the University of Cambridge. He is interested in programming languages and verification with a focus program logics for weak memory, program logics for concurrency, compiler verifications, automated verification of concurrent programs, and interactive theorem proving.

Abstract

Compilers form an integral component of the software development ecosystem. Compiler writers must understand the specification of source and target languages to design sophisticated algorithms that transform programs while preserving semantics. Unfortunately, compiler bugs in mainstream compilers are common. Compiler bugs can manifest as crashes during compilation, or, much worse, result in the silent generation of incorrect programs. Such mis-compilations can introduce subtle errors that are difficult to diagnose and generally puzzling to software developers.

The talk will describe the problems in developing peephole optimizations that perform local rewriting to improve the efficiency of LLVM code. These optimizations are individually difficult to get right, particularly in the presence of undefined behavior; taken together they represent a persistent source of bugs. The talk will present Alive, a domain-specific language for writing optimizations and for automatically either proving them correct or else generating counterexamples. A transformation in Alive is shown to be correct automatically by encoding the transformation into constraints, which are automatically checked for validity using a Satisfiability Modulo Theory (SMT) solver. Furthermore, Alive can be automatically translated into C++ code that is suitable for inclusion in an LLVM optimization pass.

Alive is based on an attempt to balance usability and formal methods; for example, it captures—but largely hides—the detailed semantics of three different kinds of undefined behavior in LLVM. We have translated more than 300 LLVM optimizations into Alive and, in the process, found that eight of them were wrong. I will conclude the talk highlighting the lessons learned and the challenges in incorporating lightweight formal methods in the tool-chain of the compiler developer.

Bio

Santosh Nagarakatte is an Assistant Professor of Computer Science at Rutgers University. He obtained his PhD from the University of Pennsylvania. His research interests are in Hardware-Software Interfaces spanning Programming Languages, Compilers, Software Engineering, and Computer Architecture. His papers have been selected as IEEE MICRO TOP Picks papers of computer architecture conferences in 2010 and 2013. He has received the NSF CAREER Award in 2015, PLDI 2015 Distinguished Paper Award, and the Google Faculty Research Award in 2014 for his research on incorporating lightweight formal methods for LLVM compiler verification.

Abstract

Programmers use profilers to understand the performance characteristics of their programs and to focus on those pieces whose improvement may yield the largest gains. A conventional profiler measures the time that a program spends in functions, methods, expressions, and statements. Racket's novel feature-specific profiler supplements this information with timings of instances of linguistic features. This paper reports the results of a successful reproducibility effort to adapt feature-specific profiling to the R programming language. Specifically, the paper demonstrates how easy and effective it is to add the necessary language support, that the approach usefully enhances the information produced by a classical profiler, and that the additional overhead is tolerable.

Bio

Leif Andersen is a second year Ph.D. student at Northeastern University, studying programming language with Matthias Felleisen.

Abstract

Performance clearly matters to users. The most common software update on the AppStore *by far* is "Bug fixes and performance enhancements." Now that Moore's Law Free Lunch has ended, programmers have to work hard to get high performance for their applications. But why is performance so hard to deliver?

I will first explain why our current approaches to evaluating and optimizing performance don't work, especially on modern hardware and for modern applications. I will then present two systems that address these challenges. Stabilizer is a tool that enables statistically sound performance evaluation, making it possible to understand the impact of optimizations and conclude things like the fact that the -O2 and -O3 optimization levels are indistinguishable from noise (unfortunately true).

Since compiler optimizations have largely run out of steam, we need better profiling support, especially for modern concurrent, multi-threaded applications. Coz is a novel "causal profiler" that lets programmers optimize for throughput or latency, and which pinpoints and accurately predicts the impact of optimizations. Coz's approach unlocks numerous previously unknown optimization opportunities. Guided by Coz, we improved the performance of Memcached by 9%, SQLite by 25%, and accelerated six Parsec applications by as much as 68%; in most cases, these optimizations involved modifying under 10 lines of code.

This talk is based on work with Charlie Curtsinger published at ASPLOS 2013 (Stabilizer) and SOSP 2015 (Coz), where it received a Best Paper Award.

Bio

Emery Berger is a Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, the flagship campus of the UMass system. He graduated with a Ph.D. in Computer Science from the University of Texas at Austin in 2002. Professor Berger has been a Visiting Scientist at Microsoft Research (7 times) and at the Universitat Politecnica de Catalunya (UPC) / Barcelona Supercomputing Center (BSC).

Professor Berger’s research spans programming languages, runtime systems, and operating systems, with a particular focus on systems that transparently improve reliability, security, and performance. He is the creator of a number of influential software systems including Hoard, a fast and scalable memory manager that accelerates multithreaded applications (used by companies including British Telecom, Cisco, Crédit Suisse, Reuters, Royal Bank of Canada, SAP, and Tata, and on which the Mac OS X memory manager is based); DieHard, an error-avoiding memory manager that directly influenced the design of the Windows 7 Fault-Tolerant Heap; and DieHarder, a secure memory manager that was an inspiration for hardening changes made to the Windows 8 heap.

His honors include a Microsoft Research Fellowship, an NSF CAREER Award, a Lilly Teaching Fellowship, a Most Influential Paper Award at OOPSLA 2012, a Google Research Award, a Microsoft SEIF Award, a Best Artifact Award at PLDI, and Best Paper Awards at FAST, OOPSLA, and SOSP; he was named an ACM Senior Member in 2010. Professor Berger is currently a Member of the SIGPLAN Executive Committee and an Associate Editor of the ACM Transactions on Programming Languages and Systems, and will serve as Program Chair for PLDI 2016.

Abstract

Computer networks do not simply connect machines together, but run several applications on network devices, such as load balancers, intrusion detection systems, authentication portals, and more. Historically, these applications were black-boxes running on proprietary hardware, but software-defined networking (SDN) now allows anyone to write their own programs using open networking protocols (e.g., OpenFlow). So, what are the right abstractions for programming networks? This talk will try to address this question in three ways.

First, we present a syntactic theory of network forwarding called NetKAT, which supports equational reasoning about network-wide behavior. Using NetKAT, programmers can ask and answer questions like, "Can A communicate with B?", "Does all traffic traverse my intrusion detection system?", "Is there a loop in my network?", and so on.

Second, we present a fast and efficient compiler for NetKAT. Although several network compilers already exist, they are unusable on even moderately sized networks. Using new data structures and compilation algorithms, our new compiler is two orders of magnitudes faster than prior work and scales to large datacenter networks.

Finally, we consider the problem of building a reliable runtime system for NetKAT. NetKAT abstracts away several low-level details of networking hardware. Although this is a boon for the network programmer, the burden now shifts to us to engineer abstractions correctly. We present a Coq-certified runtime system that is proven correct with respect to a detailed operational model software- defined networks.

Bio

Arjun Guha is an assistant professor of Computer Science at UMass Amherst. He enjoys tackling problems in systems using the tools and principles of programming languages. Apart from network programming, he has worked on Web security and system configuration languages. He received a PhD in Computer Science from Brown University in 2012 and a BA in Computer Science from Grinnell College in 2006.

Abstract

In geo-replicated distributed data stores, the need to ensure responsiveness in the face of network partitions and processor failures results in implementations that provide only weak (so-called eventually consistent) guarantees on when data updated by one process becomes visible to another. Applications must be carefully constructed to be aware of unwanted inconsistencies permitted by such implementations (e.g., having negative balances in a bank account, or having an item appear in a shopping cart after it has been removed), but must balance correctness concerns with performance and scalability needs. Because understanding these tradeoffs requires subtle reasoning and detailed knowledge about the underlying data store, implementing robust distributed applications in such environments is often an error-prone and expensive task.

To overcome these issues, this talk presents a declarative programming model for eventually consistent data stores called Quelea. The model comprises a contract language, capable of defining fine-grained application-level consistency properties for replicated data types (and transactions over objects of these types), and a contract enforcement system to analyze contracts and automatically generate the appropriate consistency protocol for the method protected by the contract. By doing so, Quelea enables programmers to reason compositionally about consistency from the perspective of high-level application requirements, not low-level implementation features.

This is joint work with Gowtham Kaki and K.C. Sivaramakrishnan.

Bio

Suresh Jagannathan is a Professor of Computer Science at Purdue University where he has been on leave since September 2013, serving as a program manager in the Information Innovation Office at DARPA. He has also been a visiting faculty at Cambridge University, where he spent a sabbatical year in 2010; and, prior to joining Purdue, was a senior research scientist at the NEC Research Institute in Princeton, N.J. He received his Ph.D from MIT.

His research interests are in programming languages generally, with specific focus on compilers, functional programming, program verification, and concurrent and distributed systems. At DARPA, he manages programs on probabilistic programming and machine learning (PPAML), program synthesis and repair leveraging predictive analytics over large software corpora (MUSE), and self-adaptive software through resource-aware analyses, runtimes, and architectures (BRASS).

Abstract

Hop.js is a multitier extension of JavaScript. It allows a single JavaScript program to describe the client-side and the server-side components of a Web application. Its runtime environment ensures a consistent execution of the application on the server and on the client. This talk will shows its minimal set of JavaScript extensions that makes Web programming easier. It will present its runtime environment, with an emphasize on the handling of server-side parallelism.

Bio

Manuel is a researcher at INRIA Sophia Antipolis, he used to work on Scheme.

Abstract

This talk is about scalloc, a fast, multicore-scalable, low-fragmentation memory allocator and selfie, a 4000-line implementation of a tiny self-compiling C compiler and a tiny self-executing MIPS emulator for teaching systems engineering. Scalloc is a typical example of a very complex, multi-year research effort while selfie is, at least for now, a purely educational, many-year effort in teaching compiler, operating system, and virtual machine design based on a single, highly principled software platform. So far scalloc and selfie only share the passion of their authors and are otherwise two distinct projects. Yet earlier versions of selfie, before they were even identified as such, were instrumental in bringing up the generation of students who did scalloc. The main ideas behind scalloc are: uniform treatment of small and big objects through so-called virtual spans, efficiently and effectively reclaiming free memory through fast and scalable global data structures, and constant-time (modulo synchronization) allocation and deallocation operations that trade off memory reuse and spatial locality without being subject to false sharing. The main ideas behind selfie are: a compiler written in and for a tiny subset of C called C* which uses the dereferencing * operator of C for memory access but lacks data structures and many other features and a MIPS emulator written in C* that can execute itself. Both are combined and extended by students to do very cool stuff.

Bio

Christoph Kirsch is Professor at the University of Salzburg, Austria. From 1999 to 2004 he worked as Postdoctoral Researcher at UC, Berkeley. He later returned to Berkeley as Visiting Scholar (2008-2013) and Visiting Professor (2014) as part of a collaborative research effort in Cyber-Physical Systems. His most recent research interests are in concurrent data structures, memory management, and so-called spatial programming. Dr. Kirsch co-invented embedded programming languages and systems such as Giotto, HTL, and the Embedded Machine, and more recently co-designed high-performance, multicore-scalable concurrent data structures and memory management systems. He co-founded the International Conference on Embedded Software (EMSOFT) and served as ACM SIGBED chair from 2011 until 2013 and ACM TODAES associate editor from 2011 until 2014.

Abstract

Most programs interact with the world: via graphical user interfaces, networks, etc. This form of interactivity entails concurrency, and concurrent program components must coordinate their computations. This talk will present Syndicate, a design for a coordinated, concurrent programming language. Each concurrent component in Syndicate is a functional actor that participates in scoped conversations. The medium of conversation arranges for message exchanges and coordinates access to common knowledge. As such, Syndicate occupies a point in design space halfway between actors and threads.

Bio

Tony is a graduate student working with Matthias.

Abstract

Memory corruption plagues systems since the dawn of computing. With the rise of defense techniques like stack cookies, ASLR, and DEP, attacks have become much more complicated, yet control-flow hijack attacks are still prevalent. Attacks leverage code reuse attacks, often using some form of information disclosure. Stronger defense mechanisms have been proposed but none have seen wide deployment so far (i) due to the time it takes to deploy a security mechanism, (ii) incompatibility with specific features, and (iii) most severely due to performance overhead. In the talk, we evaluate the security benefits and limitations of the status quo and look into upcoming defense mechanisms (and their attacks). Control-Flow Integrity (CFI) and Code-Pointer Integrity (CPI) are two of the hottest upcoming defense mechanisms. CFI guarantees that the runtime control flow follows the statically determined control-flow graph. An attacker may reuse any of the valid transitions at any control flow transfer. CPI on the other hand is a dynamic property that enforces memory safety guarantees like bounds checks for code pointers by separating code pointers from regular data. We will discuss differences and advantages/disadvantages of both approaches, especially the security benefits they give under novel attacks like Counterfeit Object-Oriented Programming (COOP) and Control-Flow Bending (CFB). COOP reuses complete functions as gadgets and CFB bends the control flow along valid but unintended paths in the control-flow graph of a program.

Bio

Mathias Payer is a security researcher and an assistant professor in computer science at Purdue university. His interests are related to system security, binary exploitation, user-space software-based fault isolation, binary translation/recompilation, and (application) virtualization. His research focuses on protecting applications even in the presence of vulnerabilities, with a focus on memory corruption. Before joining Purdue in 2014 he spent two years as PostDoc in Dawn Song's BitBlaze group at UC Berkeley. He graduated from ETH Zurich with a Dr. sc. ETH in 2012.

Abstract

I will provide an update on the status of the Reactor project which aims to use LLVM as just-in-time compiler for the R language. I will discuss early challenges such as integration in the environment and garbage collection support.

Bio

Jan Vitek is a Professor at Northeastern, he works on programming language design and implementation.

Abstract

Kevin will discuss the details behind the implementation of completions + diagnostics, as well as some future goals re: enabling user extensibility of both the autocompletion and diagnostics systems.

Bio

Kevin is a software engineer at RStudio. He graduated from the University of British Columbia with an MSc in Statistics, with a thesis focusing on the use of non-linear mixed effects models in the analysis of yeast growth curves in gene knockout studies. Shortly thereafter, he worked doing data analysis at St. Paul's hospital in Vancouver as part of the Daley lab, and later at the Fred Hutchinson Cancer Research Center in Seattle as part of the Gottardo lab. At RStudio, Kevin primarily works on the RStudio IDE, but also maintains the R package packrat.

Abstract

R is a dynamic language for statistical computing and graphics. In recent years R has become a major framework for both statistical practice and research. This talk present a very brief outline of the R language and its evolution and describe some current efforts on improvements to the core computational engine, including work on compilation of R code, efforts to take advantage of multiple processor cores, and modifications to support working with larger data sets.

Bio

Luke Tierney is a Professor Statistics at the University of Iowa and one of the key contributors to the R project.

Abstract

Modern verification techniques are becoming ever-more powerful and sophisticated, and building tools to implement them is a time-consuming and difficult task. Writing a new verifier to validate each on-paper approach is impractical; for this reason intermediate verification languages such as Boogie and Why3 have become popular over the last decade. However, verification approaches geared around complex program logics (such as separation logic) have typically been implemented in specialised tools, since the reasoning is hard to map down to first-order automated reasoning. In practice, this means that a rich variety of modern techniques have no corresponding tool support. In this talk, I will present the new Silver intermediate verification language, which has been designed to facilitate the lightweight implementation of a variety of modern methodologies for program verification. In contrast to lower-level verification languages, Silver provides native support for heap reasoning; modes of reasoning such as concurrent separation logic, dynamic frames and rely-guarantee/invariants can be simply encoded. Silver has been developed as part of the Viper project, which provides two automated back-end verifiers for Silver programs. Since releasing our software in September last year, it has been used for (internal and external) projects to build tools for Java verification, non-blocking concurrency reasoning, flow-sensitive typing and reasoning about GPU and Linux kernel code.

Bio

Alex Summers obtained his PhD from Imperial College London in 2009, in the area of type systems and classical logics. Since then he has worked in a variety of areas concerning software correctness and verification, at Imperial College London and ETH Zurich. His research interests include developing specification techniques for different (usually concurrent) programming paradigms, and implementing these in automatic verification tools. He was recently awarded the 2015 AITO Dahl-Nygaard Junior Prize for his work on type systems and the verification of object-oriented programs.

Abstract

In this talk, I will first present a method and a tool TARA for generating succinct representations of sets of concurrent traces. In our work, we focus on trace sets that contain all correct or all incorrect permutations of events from a given trace. We represent such trace sets as Boolean combinations of happens-before ordering constraints between events. Our trace set representations can drive diverse verification, fault localization, repair, and synthesis techniques for concurrent programs. In the remainder of the talk, I will focus on the use of our representation for synchronization synthesis. This work appears in POPL 2015 and CAV 2015, and is joint work with Pavol Cerny, Ed Clarke, Ashutosh Gupta, Tom Henzinger, Arjun Radhakrishna, Leonid Ryzhyk and Thorsten Tarrach.

Bio