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Colin Graber · Yuan-Ting Hu · Tiantian Fang · Jessica Hamrick · Giorgio Giannone · John Co-Reyes · Boyang Deng · Eric Crawford · Andrea Dittadi · Peter Karkus · Matthew Dirks · Rakshit Trivedi · Sunny Raj · Javier Felip Leon · Harris Chan · Jan Chorowski · Jeff Orchard · Aleksandar Stanić · Adam Kortylewski · Ben Zinberg · Chenghui Zhou · Wei Sun · Vikash Mansinghka · Chun-Liang Li · Marco Cusumano-Towner

Fri Dec 13 02:40 PM -- 03:30 PM (PST) @
in Perception as generative reasoning: structure, causality, probability »

Author Information

Colin Graber (University of Illinois at Urbana-Champaign)
Yuan-Ting Hu (University of Illinois Urbana-Champaign)
Tiantian Fang (University of Illinois Urbana-Champaign)
Jessica Hamrick (DeepMind)
Giorgio Giannone (NNAISENSE)

Science is built up with data, as a house is with stones. But a collection of data is no more a science than a heap of stones is a house. (J.H. Poincaré)

John Co-Reyes (UC Berkeley)

Interested in solving intelligence. Currently working on hierarchical reinforcement learning and learning a physical intuition of the world.

Boyang Deng (Google)
Eric Crawford (McGill University)
Andrea Dittadi (Technical University of Denmark)
Peter Karkus (NUS)
Matthew Dirks (University of British Columbia)
Rakshit Trivedi (GEORGIA INSTITUTE OF TECHNOLOGY)
Sunny Raj (University of Central Florida)
Javier Felip Leon (Intel Corporation)
Harris Chan (University of Toronto, Vector Institute)
Jan Chorowski (University of Wroclaw)
Jeff Orchard (University of Waterloo)

Jeff Orchard received degrees in applied mathematics from the University of Waterloo (BMath) and the University of British Columbia (MSc), and received his PhD in Computing Science from Simon Fraser University in 2003. Since then, he has been a faculty member in the Cheriton School of Computer Science at the University of Waterloo. His main research focus is on computational neuroscience and artificial intelligence, using mathematical models and computer simulations of neural networks to understand how the brain works. He has also published research papers in image processing, medical imaging, and graphics.

Aleksandar Stanić (Swiss AI lab IDSIA)
Adam Kortylewski (Johns Hopkins University)
Ben Zinberg (MIT)
Chenghui Zhou (Carnegie Mellon University)
Wei Sun (University of Waterloo)
Vikash Mansinghka (Massachusetts Institute of Technology)

Vikash Mansinghka is a research scientist at MIT, where he leads the Probabilistic Computing Project. Vikash holds S.B. degrees in Mathematics and in Computer Science from MIT, as well as an M.Eng. in Computer Science and a PhD in Computation. He also held graduate fellowships from the National Science Foundation and MIT’s Lincoln Laboratory. His PhD dissertation on natively probabilistic computation won the MIT George M. Sprowls dissertation award in computer science, and his research on the Picture probabilistic programming language won an award at CVPR. He served on DARPA’s Information Science and Technology advisory board from 2010-2012, and currently serves on the editorial boards for the Journal of Machine Learning Research and the journal Statistics and Computation. He was an advisor to Google DeepMind and has co-founded two AI-related startups, one acquired and one currently operational.

Chun-Liang Li (Carnegie Mellon University)
Marco Cusumano-Towner (Massachusetts Institute of Technology)

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