Programming Models for Estimates, Approximation, and Probabilistic Reasoning

Many emerging applications produce and use estimates in domains as diverse as probabilistic reasoning, machine learning, data analytics, approximate computing, and sensor programming. This session will focus on programming language models for expert developers and the masses (application programmers without statistics background). In particular, we will explore how well current languages support developers in producing estimation models, computing with estimates, and reasoning about what the resulting programs mean.

Speaker Details

Kathryn S McKinley is a principal researcher at Microsoft in the area of programming languages and systems, design and implementation. She has served as Editor-in-Chief of TOPLAS, as Program Chair of five conferences (ASPLOS, PACT, PLDI, ISMM, CGO), on 50+ program committees, and is currently a Board member of ISAT, CRA, and CRA-W. She is an ACM and IEEE Fellow.

Noah D. Goodman is assistant professor of Psychology, Linguistics (by courtesy), and Computer Science (by courtesy) at Stanford University. He studies the computational basis of human thought, merging behavioral experiments with formal methods from statistics and programming languages. He received his Ph.D. in mathematics from the University of Texas at Austin in 2003. In 2005, he entered cognitive science, working as postdoc and research scientist at MIT. In 2010 he moved to Stanford where he runs the Computation and Cognition Lab. CoCoLab studies higher-level human cognition including language understanding, social reasoning, and concept learning; the lab also works on applications of these ideas and enabling technologies such as probabilistic programming languages.

Michael Carbin is researcher at Microsoft Research, a visiting scientist at MIT, and will be joining MIT as an assistant professor in January of 2016. His research interests include the theory, design, and implementation of programming systems, including languages, program logics, static and dynamic program analyses, runtime systems, and mechanized verifiers. Carbin’s recent research has focused on the design and implementation of programming systems that deliver improved performance and resilience by incorporating approximate computing and self-healing. His research on verifying the reliability of programs that execute on unreliable hardware has received best paper awards at leading programming languages conferences (OOPSLA 2013 and OOPSLA 2014). His undergraduate research at Stanford University received the Wegbreit Prize for Best Computer Science Undergraduate Honors Thesis. As a graduate student at MIT, Carbin received the MIT Lemelson Presidential and Microsoft Research Graduate Fellowships.

Dan Grossman has been a faculty member in the Department of Computer Science & Engineering at the University of Washington since 2003. He holds the J. Ray Bowen Professorship for Innovation in Engineering Education. His research interests are in several areas of programming languages, and he has collaborated for several years with colleagues in computer architecture on better approaches to concurrent programming and approximate programming. Dan completed his Ph.D. at Cornell University and his undergraduate studies at Rice University.

Todd Mytkowicz is a researcher at Microsoft Research. His research focuses on creating abstractions that help programmers easily express complex problems and yet are sufficiently constrained to deliver efficient and powerful implementations. Mytkowicz has a PhD in computer science from the University of Colorado at Boulder and enjoys mountain biking, skiing, and spending time with his family.

Series:: Microsoft Research Faculty Summit
Date:: July 24, 2015
Speakers:: Dan Grossmann, Kathryn McKinley, Michael Carbin, Noah Goodman, and Todd Mytkowicz
Affiliation:: Microsoft Research, Stanford University, University of Washington