Dr.-Ing. Ali Bereyhi
University of Toronto
I am a postdoctoral research fellow at Wireless Computing Lab (WCL) in the Department of Electrical and Computer Engineering at the University of Toronto. Prior to that, I was postdoctoral researcher and lecturer at the Institute for Digital Communications (IDC) in the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany. I have received my PhD (in German Doktor der Ingenieurwissenschaft) with distinction from FAU in 2020, supervised by Prof. Dr.-Ing. Ralf R. Müller and Prof. Dr. Hermann Schulz-Baldes.
My main area of interest is to explore the connections between theoretical physics and the theory of learning. This has been the topic of my doctoral dissertation, entitled Statistical Mechanics of Regularized Least Squares, and is still being my main research focus.
Statistical Learning Theory
Bayesian Inference, Machine Learning, Regression, Approximate Message Passing, Federated Learning
Statistical Signal Processing
Compressive Sensing, Detection Theory, Estimation Theory, Sensor Fusion
Statistical Mechanics and Its Connection to Information Processing
Replica Method, Information-theoretic Interpretation of Physical Models, Large Deviations Theory, Mean-field Theory, Random Matrix Theory
Network Information Theory, Information Theoretic Secrecy, Secret Key Agreement
Computation and Communications
Computation Coding, Distributed Coded Computing
MIMO Precoding, Signal Processing for Communications
[June 2023] I am now on the Mathematics Genealogy Project. Check out my page here!
[May 2023] My course "Information Theory and Coding" in Winter 2022-23 has been selected as the Best Lecture in the Faculty of Engineering at FAU
[Feb 2023] I am joining the Wireless Computing Lab (WCL) in the Department of Electrical and Computer Engineering at the University of Toronto
[Nov 2022] The book "Compressed Sensing in Information Processing" has been published. This book has been prepared throughout the last phase of the DFG-funded special session Compressed Sensing in Information Processing (CoSIP). Checkout our contribution in Chapter 5. In this chapter, we have given a comprehensive introduction to the replica method and its application to asymptotic analysis of Bayesian-type algorithms.
[Jul 2022] I am giving an invited talk at the Random Matrices and Random Landscapes (RMRL 2022) conference in honor of Yan Fyodorov's 60th birthday. The talk is entitled "Bayesian Inference with Nonlinear Generative Models". The slides of my presentation can be found here.
For My Former Students at FAU
The lecture materials of my courses at FAU can still be accessed through StudOn. You can also download the lecture-notes here. The lecture-notes have been updated last time on Winter 2022-23. The version on GitHub is the final version, and hence could contain some changes compared to the older versions on the StudOn pages of previous semesters.