Lior Seeman is a Ph.D. student at Cornell University and earned his bachelor’s degree in computer science and management from Tel Aviv University. His research lies at the intersection between computer science, economics, social science and cognitive science. More specifically, Seeman has been working on using ideas from computational complexity to model people’s bounded rationality, and using that to better understand their decision-making processes and social interaction. Seeman’s work has shown that modeling people as computationally bounded agents can help us better understand and predict their behavior. He has also studied how viewing agents as computationally bounded can affect our understanding of fundamental problems at the intersection of game theory and computer science, such as the complexity of computing Nash equilibrium in repeated games. Finally, Seeman also interned in Google research, where he developed a new approach for the well-studied problem of influence maximization in social networks, which takes advantage of some structural properties of the network. Seeman’s work has appeared in conferences such as FOCS, ITCS, AAAI and VLDB.