Zachary S. Siegel is a doctoral student in the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology, where he works at the intersection of robotics, cognitive science and artificial intelligence.
Siegel’s research goal is to build machines that learn and reason more like people — systems that can learn from limited data and generalize to new situations by combining robot planning and Bayesian inference. He is particularly interested in combinatorial generalization: the human capacity to compose known skills in novel ways to solve previously unseen problems without additional demonstrations. At MIT, he is advised by Leslie P. Kaelbling, Tomás Lozano-Pérez and Joshua B. Tenenbaum.
Siegel graduated summa cum laude from Princeton University with a B.S.E. in computer science and a minor in philosophy, receiving honors including Tau Beta Pi, Sigma Xi and the Outstanding Computer Science Independent Work Prize. His senior thesis, advised by Tom Griffiths and Jacob Andreas, investigated how humans infer the goals of others in open-ended, real-world environments. He demonstrated how Bayesian inference serves as an accurate model of people’s goal predictions by comparing partial observations to a learned library of possible plans weighted by their prior likelihood.
Raised in Scarsdale, New York, Siegel is the founder and chairman of the board of the Youth Passion Project, a 501(c)(3) nonprofit organization that gives high school students a platform to teach live, online classes to younger students across the country. Over the past six years, the organization has reached more than 5,000 students.
Siegel has been deeply involved in teaching and mentorship throughout his academic career. Through his research and educational pursuits, he aims to advance not only the scientific understanding of intelligence, but also the institutions and communities that sustain rigorous, truth-seeking inquiry in service of society.
