Jamie Morgenstern is a Ph.D. student at Carnegie Mellon University. She did her undergraduate work at the University of Chicago, where she received a B.S. with honors in computer science and a B.A. in mathematics. Morgenstern’s current research focuses on market algorithms, including auctions and fair division, as well as issues of learning and privacy which arise in mechanism design. She also works on approximation algorithms in bioinformatics. As one notable result, Morgenstern and her coauthors have shown, with a heterogeneous divisible good, there always exists an envy-free allocation with social welfare at least as high as the best equitable allocation. One of her current projects involves analyzing how providing privacy-preserving information about players’ actions can improve social welfare and avoid disastrous outcomes in natural multi-agent settings motivated by financial decision-making. She is also developing methods that can be used for learning preferences of bidders in auctions from only their final outcomes.