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Shane Barratt · Alex Groce · Qi Yan · Sapan Agarwal · Fabian Offert · Bogdan Kulynych · Housam Khalifa Bashier Babiker · Petar Stojanov · Topi Paananen · Jose Marcio Luna · Gilmer Valdes · Jacqueline A Mauro · Daniel Chen · Baruch Schieber · Randolph Goebel · Jacob Bien

Thu Dec 07 03:40 PM -- 04:15 PM (PST) @

Author Information

Shane Barratt (Stanford University)
Alex Groce (Northern Arizona University)

Alex Groce received his Ph.D. in computer science from Carnegie Mellon University in 2005, and B.S. degrees in computer science and multidisciplinary studies (with a focus on English literature) from North Carolina State University in 1999. He was a core member of the Laboratory for Reliable Software at NASA’s Jet Propulsion Laboratory, and taught classes on software testing at the California Institute of Technology. His activities at JPL included a role as lead developer and designer for test automation for the Mars Science Laboratory mission’s internal flight software test team, and lead roles in testing file systems for space missions. In 2009, he joined the faculty in Computer Science at Oregon State University, and was promoted to Associate Professor in 2015. In 2017, he joined the faculty of the new School of Informations, Computing, and Cyber Systems at Northern Arizona University, to focus on software testing techniques for ensuring security and reliability of complex systems, especially embedded, scientific, and systems software. His research interests are in software engineering, particularly testing, model checking, static analysis, automated debugging, and execution understanding. He focuses on software engineering from an “investigative” viewpoint, with an emphasis on the execution traces that programs produce — software engineering as the art and science of building programs with a desired set of executions. His recent work has resulted in a DSL and (he hopes) usable and powerful testing tool for Python, the TSTL system: https://github.com/agroce/tstl His interests in machine learning are primarily as a user of applied machine learning in order to improve software testing; a secondary interest is in testing of ML systems.

Qi Yan (Tsinghua University)
Sapan Agarwal (Sandia National Laboratories)
Fabian Offert (University of California, Santa Barbara)

PhD candidate at UCSB studying the aesthetics of artificial intelligence. Special interest in generative approaches and interpretability/visualization.

Bogdan Kulynych (Google / EPFL)

Graduate student. Former intern at CERN, currently intern at Google.

Housam Khalifa Bashier Babiker (University of Alberta)
Petar Stojanov (Carnegie Mellon University)
Topi Paananen (Aalto University)
Jose Marcio Luna (University of Pennsylvania)
Gilmer Valdes (University of California, San Francisco)
Jacqueline A Mauro (Carnegie Mellon University)

I am studying for my PhD in Statistics, joint with Public Policy, at Carnegie Mellon University under Edward Kennedy. I work on social science problems from International Trade to Criminology, developing new nonparametric causal inference methods that can be used by practitioners across a variety of fields. Our estimators lean on developments in Machine Learning to create flexible yet robust estimates of causal effects. I aim to develop new methods of analysis that can tell us about the effects of policy-interventions that matter. For example, how does the distance from home affect prisoners' chances of staying out of jail? How does an open trade policy affect workers in small exporting countries? Before gradute school, I worked as a Research Assistant at RAND. There, my team provided recommendations to the Air Force about the effects and sources of stress for the ICBM force. I also worked on a team which developed a nationwide survey for victims of crime. I graduated from Barnard College with a BA in Economics in 2010, and earned my MA from Columbia University in Quantitative Methods in Social Sciences in 2011.

Daniel Chen (Toulouse School of Economics / The Institute for Advanced Study in Toulouse)

Daniel Chen received his BA and MS in applied math and economics from Harvard College (1999, summa cum laude) and his JD (2009) from Harvard Law School. He earned his PhD in economics from MIT (2004). He is a professor at the Institute for Advanced Study in Toulouse / Toulouse School of Economics, Senior Research Associate/Fellow at LWP at Harvard Law School, and Project Advisor at NYU Courant Institute of Mathematical Sciences Center for Data Science. He was previously Chair of Law and Economics and co-founder of the Center of Law and Economics at ETH Zurich, Assistant Professor of Law, Economics, and Public Policy at Duke University, and Kauffman Fellow at the University of Chicago Law School. He has lines of research on law and legitimacy, how market forces interact with moral beliefs, behavioral influences on judicial decision-making, and measuring the consequences of law via the random assignment of judges. He has papers published in Econometrica, Journal of Political Economy, Quarterly Journal of Economics, American Economic Review, RAND Journal of Economics, and several law reviews. He maintains an interest in methodology through high-dimensional statistical approaches for causal inference and through the development of oTree---an open source platform for online, lab, and field experiments.

Baruch Schieber (IBM Research AI)
Randolph Goebel (University of Alberta, Alberta Machine Intelligence Institute)

R.G. (Randy) Goebel is currently professor and chair in the Department of Computing Science at the University of Alberta He received the B.Sc. (Computer Science), M.Sc. (Computing Science), and Ph.D. (Computer Science) from the Universities of Regina, Alberta, and British Columbia, respectively. Professor Goebel's research is focused on the theory and application of intelligent systems. His theoretical work on abduction, hypothetical reasoning and belief revision is internationally well know, and his recent application of practical belief revision and constraint programming to scheduling, layout, and web mining is now having industrial impact. He is one of the founders of the Alberta Ingenuity Centre for Machine Learning (AICML), and is now working on applications of machine learning to various problems, including web visualization and scheduling. Randy has previously held faculty appointments at the University of Waterloo and the University of Tokyo, and is actively involved in academic and industrial collaborative research projects in Canada, Australia, Malaysia, Europe and Japan.

Jacob Bien (University of Southern California)

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