Katie Bouman - California Institute of Technology

Katherine L. (Katie) Bouman is a Rosenberg Scholar and an assistant professor in the Computing and Mathematical Sciences, Electrical Engineering, and Astronomy Departments at the California Institute of Technology. Before joining Caltech, she was a postdoctoral fellow in the Harvard-Smithsonian Center for Astrophysics. She received her Ph.D. in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT in EECS, and her bachelor's degree in Electrical Engineering from the University of Michigan.

Bob Carpenter - Flatiron Institute

Bob Carpenter is a research scientist at Flatiron Institute’s Center for Computational Mathematics.  He works on probabilistic programming, computational statistics, and applied Bayesian statistics.  Bob co-founded the Stan project and designed its probabilistic programming language (Columbia University).  Before returning to academia, Bob learned to code in industry (SpeechWorks, Alias-i) working on natural language processing and speech. In addition to computational linguistics, Bob studied programming languages as a Ph.D. student  (Edinburgh) and professor (Carnegie Mellon).

Brian Patton - Google

Brian Patton has been a software engineer at Google since 2006, focused in recent years on developing TF Probability, now managing that team in Google Research, and using the toolkit across several applied projects within Google. He’s worked in years past on Google Analytics, Trends, and Play, as well as research into speech and audio source separation.

Omesh Tickoo - Intel

Omesh is a Principal Engineer and Research Manager in Intel Labs. His research is focused on next generation algorithms and platforms solutions for human-computer interaction using Computer Vision and associated sensing modalities. Omesh’s current research interests include probabilistic computing, interactive multi-modal scene understanding and contextual learning. In the past Omesh has worked on projects related to low power hardware acceleration, contextual knowledge management and systems optimization for different Intel platform solutions. Omesh received his PhD from Rensselaer Polytechnic Institute for his thesis on Analysis and Improvement of Multimedia Transmission over Wireless Networks. Omesh has authored more than 30 papers in premier international Journals and Conferences and holds more than 30 patents. Omesh has served as chair of multiple committees for IEEE conferences. He has co-organized Computational Intelligence and Soft Computing workshops alongside PACT. Omesh regularly serves as a Technical Program Committee member and reviewer for international conferences and journals.

Michael Tingley - Facebook

Michael Tingley is the engineering manager for Facebook’s Probabilistic Programming Languages team. Their goal is to platformize Bayesian modeling and analysis within Facebook, and invest energy and research into cutting-edge techniques that rely on compiler-driven analyses in order to advance the performance and reliability of universal Bayesian inference. They are currently working on building Bean Machine, a PyTorch-based PPL along with a “PPL Compiler” to leverage model structure in novel ways throughout the modeling lifecycle. In turn, this enables techniques like compositional inference, higher-order gradients, and transpilation to faster backends. Their team is also heavily involved in applying PPLs to numerous domains within Facebook, from maximizing ads efficacy to combating misinformation and inauthentic behavior. They will be open sourcing Bean Machine later this year and look forward to your feedback!

John Winn - Microsoft Research

John Winn is a Senior Principal Researcher in machine learning at Microsoft Research Cambridge. His main research interests are ML for knowledge extraction, machine vision, probabilistic programming and ML for healthcare. At Microsoft, he co-created the Infer.NET probabilistic programming system. He is also author of the Model-Based Machine Learning book. Before joining Microsoft, he was a PhD. student in the Inference Group at the Cavendish Laboratory, supervised by Chris Bishop and David MacKay. He has previously been a member of the Learning and Vision Group at the MIT AI Lab.

Veronica Weiner - MIT (moderator)

Veronica Weiner has been a Director of Special Projects and Research Scientist at the MIT Probabilistic Computing Project during the past five years and managing partner of a probabilistic programming startup company. Her research in probabilistic programming has focused on automated Bayesian data modeling. She has been part of the team developing the open source probabilistic programming software InferenceQL and the MIT Inference Stack. Previously, she was a Director of Strategic Partnerships and Research Scientist at the MIT McGovern Institute for Brain Research and co-founded a biotech company whose lead drug program was acquired. She has a Ph.D. and S.B.s from MIT.