Rachel Cummings is a Ph.D. student in computing and mathematical sciences at the California Institute of Technology. She received B.A. degrees in mathematics and economics from the University of Southern California and her M.S. degree in computer science from Northwestern University. Her research interests lie in the intersection of computer science and economics, specifically problems surrounding algorithmic game theory, data privacy and learning theory. In one line of work, Cummings and her coauthors aim to understand what an observer can learn about an individual by repeatedly observing the individual making decisions, and how the individual can strategically make choices to obscure her or his true information. Cummings also works on designing markets for data, where individuals are compensated for their privacy loss and incentivized to truthfully report their data. She has published papers in the proceedings of EC, ITCS, AAAI, DISC and DNA. Her paper also received the best paper award at DISC 2014.