Research

Christian is a Ph.D. student in the Computer Science Department at Carnegie Mellon University, where he is advised by Tuomas Sandholm. Christian’s research lies at the intersection of computer science, economics, and operations research. He is particularly focused on games, decision making, and markets. To achieve scalability in these settings, he often draws on techniques from artificial intelligence and mathematical programming.

Research Summary

Christian’s two main research thrusts are on the topics of sequential game solving and optimization methods for markets. In practice, large-scale game solving relies on two techniques: abstraction and iterative equilibrium finding. Christian’s work has focused on developing rigorous theoretical foundations of both techniques, as well as practical algorithmic improvements. He applies these techniques to areas such as wi-fi jamming, biology, and recreational games such as poker. In his research on markets, Christian focuses on how methods from the mathematical programming and artificial intelligence literatures can be used to facilitate large-scale markets. This has led to work in the area of revenue-enhancement in practically-inspired auction settings, as well as pricing and inference in combinatorial prediction markets.