Alexander Bogatskiy

Alexander joined the University of Chicago Data Science Institute in October 2024. He is broadly interested in developing physics-informed methods for machine learning with applications in science. In particular, some of his work focused on equivariant neural network architectures for particle physics in collaboration with David Miller’s group at the Physics Department. He holds a PhD in Physics from the University of Chicago and was previously a research fellow at the Simons Foundation. His background is in mathematical physics, theoretical physics, and he is also interested in physics education. Outside of work, his interests include history, politics, philosophy, cycling, cooking, and photography.