The Data-Fusion Problem: Causal Inference and Reinforcement Learning

Elias Bareinboim is a PhD candidate in Computer Science at UCLA advised by Judea Pearl. He works on the problem of generalizability in causal inference, and more specifically proposed solutions for the problems of selection bias, fusion of experimental and non-experimental knowledge, and external validity (transfer of causal knowledge) in non-parametric settings. Recently, Elias received the "Yahoo Key Scientific Challenges Award 2012" (area of Statistics) and Dissertation Year Fellowship (2013-2014) from UCLA. He holds B.Sc. and M.Sc. degrees in Computer Science from Federal University of Rio de Janeiro, Brazil, where he worked in the areas of Complex Networks, Artificial Intelligence, and Bioinformatics.