ABSTRACT:

Humans mentally model the causal structure of the world, and manipulate these models to reason: we infer plausible causes of events, construct explanations, and imagine worlds that could have been but never were. Humans build these models from very small amounts of data, both passively observed and actively sought through experimentation. In this talk, I will present my efforts to provide computational accounts of these remarkable feats of inference, and make the case that this offers a promising approach to address hard problems in data science and artificial intelligence.

BIO:

 Zenna Tavares is a postdoctoral researcher in the Computer Science Artificial Intelligence Lab (CSAIL) in the Massachusetts Institute of Technology, supervised by Armando Solar Lezama.  Zenna received his Ph.D. this year from the Department of Brain and Cognitive Sciences at MIT.  His research consists of developing algorithms and languages for (Bayesian) probabilistic modeling and causal inference.

 Hosted by Professor Guy Van den Broeck

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