Murat A. Erdogdu

University of Toronto · Department of Computer Science · Department of Statistical Sciences Vector Institute Contact Pratt 286b, 6 King’s College Rd. Toronto, ON M5S 3G4 erdogdu@cs.toronto.edu

I am an assistant professor at the University of Toronto in departments of Computer Science and Statistical Sciences. I am a faculty member of the Machine Learning Group and the Vector Institute, and a CIFAR Chair in Artificial Intelligence. Before, I was a postdoctoral researcher at Microsoft Research - New England. I did my Ph.D. at Department of Statistics at Stanford University where I was jointly advised by Mohsen Bayati and Andrea Montanari. I have an M.S. degree in Computer Science from Stanford, and B.S. degrees in Electrical Engineering and Mathematics, both from Bogazici University.

Research Interests

• Machine Learning: Theory of learning and sampling algorithms

• Stochastic Optimization: Non-convex, convex algorithms for machine learning

• Statistics: High-dimensional statistical learning

Some Recent Papers

• N.M. Vural, L. Yu, K. Balasubramanian, S. Volgushev and M.A. Erdogdu, Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance,  2022

• K. Balasubramanian, S. Chewi, M.A. Erdogdu, A. Salim and M.S. Zhang, Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo,  2022

• S. Chewi, M.A. Erdogdu, M. Li, R. Shen and M.S. Zhang,  Analysis of Langevin Monte Carlo from Poincaré to Log-Sobolev,  2021

• H. Wang, M. Gurbuzbalaban, L. Zhu, U. Simsekli and M.A. Erdogdu, Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance,  2021