Learning; Social Networks; Game Theory
Omer Tamuz's work in Economics focuses on how people learn through social interaction, and on games and strategic behavior in networks. His mathematical interests lie in probability theory, ergodic theory, and their connections to group theory. He has also worked in machine learning and statistics, and in particular in combinatorial statistics, where the estimated parameter takes values in a finite set. Although these topics seem diverse, they are connected in many ways. For example, graphs and their geometry are important in economic learning models as well as in probability and ergodic theory. Likewise, combinatorial statistics techniques can be applied to the study of economic agents who estimate a discrete state of nature.
Before arriving at Caltech, Tamuz was a Schramm Postdoctoral Fellow at the Massachusetts Institute of Technology and Microsoft Research New England. He has worked in various software startups as an algorithm developer, chief scientist, and R&D manager. He got his M.Sc. and Ph.D. in Mathematics from the Weizmann Institute (2013) and his B.Sc. from Tel Aviv University (2006), where he was also part of an Astronomy research team studying extrasolar planets and binary star systems. He won the Michael B. Maschler Prize in Game Theory (2013), a Google fellowship in social computing (2011), the Dean's Prize for M.Sc. Students at the Weizmann Institute of Science (2011), and the Rector's Prize for undergraduate students at Tel Aviv University (2004).