Steven Wu is an assistant professor in the School of Computer Science at Carnegie Mellon University, with an appointment in the Institute for Software Research (in the Societal Computing program), and affiliated appointments with the Machine Learning Department and the Human-Computer Interaction Institute.
Wu works on algorithms and machine learning. His recent work focuses on how to make machine learning better aligned with societal values, especially privacy and fairness, and how to make machine learning more reliable and robust when algorithms interact social and economic dynamics. He studies these questions using methods and models from machine learning, statistics, optimization, differential privacy, game theory, and mechanism design.
Wu’s research has been generously supported by the National Science Foundation (NSF), an Amazon Research Award, a Google Faculty Research Award, a J.P. Morgan Faculty Award, a Facebook Research Award, and a Mozilla Research Grant.
Previously, Wu received a Ph.D. in Computer Science in 2017 from the University of Pennsylvania, where he was extremely fortunate to have been co-advised by Michael Kearns and Aaron Roth. His doctoral dissertation titled, “Data Privacy Beyond Differential Privacy” received Penn’s Morris and Dorothy Rubinoff Award for best thesis. Before joining CMU, he was an assistant professor of computer science and engineering at the University of Minnesota for two years. Before that, Wu spent a year as a post-doctoral researcher at Microsoft Research-New York City in the machine learning and algorithmic economics groups.
2017 Ph.D. Computer Science, University of Pennsylvania