Ali Bereyhi

Ali Bereyhi is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Toronto. He received his PhD with distinction in 2020 from Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) in Germany. From 2020 to 2023, he served as a Postdoctoral Research Associate and Lecturer at the Institute for Digital Communications (IDC) at FAU, teaching and researching in statistical learning theory, information theory, and signal processing. From 2023 to 2024, he was a researcher at the Wireless Computing Lab in ECE, focusing on distributed machine learning. He was selected as best lecturer in the Faculty of Engineering at FAU for his course on 'Information Theory and Coding.' His grant proposal entitled 'Bayesian Learning and Model Fitting via Nonlinear Models' won the Walter Benjamin Fellowship Award from the German Research Foundation (DFG). His research interests include machine learning, statistical signal processing, information theory, and statistical physics.

Ali Bereyhi is a researcher at the University of Toronto with expertise in Statistical Learning, Information Theory, Signal Processing, Wireless Communications, and Statistical Mechanics. His work focuses on secure communication in MIMO systems, including beamforming, antenna selection, and precoder design. He explores the application of statistical mechanics to estimation problems and develops methods for over-the-air federated learning. His research also addresses the security and robustness of massive MIMO systems against eavesdropping and interference. Bereyhi's contributions span both theoretical and practical aspects of wireless communication systems, emphasizing low-complexity architectures and performance optimization.