Murat A. Erdogdu

University of Toronto
· Department of Computer Science
· Department of Statistical Sciences
Vector Institute
Contact
Pratt 286b, 6 Kingâ€™s College Rd.
Toronto, ON M5S 3G4
erdogdu at cs.toronto dot 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 for learning and sampling algorithms
Optimization: Nonconvex, convex algorithms for machine learning
Statistics: Highdimensional data analysis, regularization and shrinkage
Some Recent Papers
L. Yu, K. Balasubramanian, S. Volgushev and M.A. Erdogdu,
An Analysis of Constant Step Size SGD in the Nonconvex Regime: Asymptotic Normality and Bias, 2020
M.A. Erdogdu and R. Hosseinzadeh,
On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness, 2020
J. Ba, M.A. Erdogdu, T. Suzuki, D. Wu and T. Zhang,
Generalization of Twolayer Neural Networks: An Asymptotic Viewpoint, 2020
X. Li, D. Wu, L. Mackey and M.A. Erdogdu,
Stochastic RungeKutta Accelerates Langevin Monte Carlo and Beyond, 2019
A. Anastasiou, K. Balasubramanian and M.A. Erdogdu,
Normal Approximation for Stochastic Gradient Descent via NonAsymptotic Rates of Martingale CLT, 2019
