Megan Shearer

I recently completed my Ph.D. in Computer Science & Engineering at the University of Michigan. My dissertation focused on modeling trading strategies in financial markets with data, simulation, and deep reinforcement learning. During my Ph.D., I spent time at FINRA, JP Morgan AI Research, and IEX. Prior to Michigan, I received my B.S. in Mathematics with minors in Economics and Computer Science from the University of Arizona.

My research lies at the intersection of computer science and economics. I use empirical game-theoretic analysis, multi-agent systems, and deep reinforcement learning to study agent interactions in simulated financial markets. I am primarily interested in generating trading strategies through deep reinforcement learning, then analyzing the market impacts in a contained, simulated environment to potentially inform financial policy.