Organizers
Denizalp Goktas is a computer science Ph.D. candidate at Brown University advised by Amy Greenwald. He was previously a research scientist intern at Google DeepMind’s Game Theory and Multiagent Team, as well as JP Morgan’s multiagent Learning and Simulation research lab. He also was a visiting scholar at UC Berkeley’s Simons institute. His interests lie at the intersection of economics and computer science. More specifically, He builds and analyzes multiagent learning algorithms for games and markets, and applies these algorithms to solve problems in economics and robotics. The ultimate goal of his research is to build large scale simulations of human behavior to improve policy-making.
Chistopher Naubert is a PhD candidate in the economics PhD program at the City University of New York Graduate Center advised by Lilia Maliar. His research interests are macroeconomics, computational economics and financial economics. He has previously done an internship at JP Morgan and Co, and holds a BA in economics and a BA in sports management from the University of Michigan.
Tom Yan is a Computer Science PhD student at Carnegie Mellon university where he is advised by Ariel Procaccia and Zack Lipton. He holds a BS and a MEng in electrical engineer and computer science from the Massachusetts Institute of Technology. His core interest is in ML, and how ML can be used in principled ways to improve and augment human decision making. One direction he is excited about is how ML can be used to operationalize economic models as applicable to macroeconomics.
Sadie Zhao is a PhD student at Harvard University advised by Yiling Chen. Before starting her PhD, she completed her undergrad in Mathematics and Computer Science at Pomona College. Her research interest lie at the intersection of economics and computer science. More specifically, she is interested in using game theory and multi-agent learning tools to model, analyze, simulate, and make decisions in the economic environments such as various markets. Currently, she is especially interested in equilibrium computation and mechanism design in dynamic economic models.
Yiling Chen is the Gordon McKay Professor of Computer Science at Harvard John A. Paulson School of Engineering and Applied Sciences. She is a member of the EconCS research group and a faculty affiliate of the Center for Research on Computation and Society (CRCS). Her interests include information elicitation and aggregation, incentive-aware machine learning, information design, human-AI collaboration, behavior experiments, algorithmic fairness, algorithmic game theory and multi-agent systems.
Amy Greenwald is a Professor of Computer Science at Brown University. Our increasingly networked world drives Amy's twin research goals: first, the effort to design and implement AI agents that interact effectively in multiagent environments; second, the effort to understand, explain, and accurately predict the dynamics of such interactions. In pursuing these goals, Amy draws from theoretical and practical sources, including a variety of disciplines such as AI, decision theory, game theory, and economics.
Jesse Perla is an Associate Professor in the Vancouver School of Economics at the University of British Columbia. His core interest is in macroeconomics and growth from the perspective of the firm, with an emphasis on the role of information diffusion. He obtained his PhD in 2013 from New York University and his undergraduate degree in applied mathematics from Columbia University in New York City.