Alon Peled-Cohen

Dr. Alon Peled-Cohen is a senior lecturer at the School of Electrical Engineering - Systems within The Iby and Aladar Fleischman Faculty of Engineering at Tel Aviv University, and a researcher at Google Research Tel-Aviv.

Dr. Peled-Cohen received his PhD in Machine Learning from the faculty of Industrial Engineering & Management at the Technion - Israel Institute of Technology under the supervision of Professor Tamir Hazan.

Dr. Peled-Cohen's research focuses on the intersection of statistical learning, online learning, and decision-making under uncertainty. His work particularly emphasizes the connections between online learning and reinforcement learning, with applications in control theory and optimization. His research addresses fundamental challenges in learning from sequential data, making decisions with limited information, and developing algorithms with provable performance guarantees. He has made significant contributions to understanding regret bounds in various learning settings and has developed novel algorithms for challenging control and optimization problems.

Analysis of Dr. Peled-Cohen's publication record reveals a strong focus on theoretical machine learning, particularly in online learning, reinforcement learning, and control theory. His work consistently addresses fundamental questions about algorithmic performance, often establishing tight regret bounds for various learning settings. A notable theme throughout his research is the application of optimization techniques to challenging learning problems, with several papers focusing on linear quadratic control and Markov decision processes. His collaborations span multiple institutions and include prominent researchers in the machine learning community.

Dr. Peled-Cohen has published in top-tier machine learning conferences including ICML, NeurIPS, UAI, and AAAI. His research has addressed important problems in online learning, reinforcement learning, and control theory, contributing both theoretical insights and practical algorithms.

As a senior lecturer at Tel Aviv University and researcher at Google Research Tel-Aviv, Dr. Peled-Cohen bridges academic research with practical applications. His work has implications for various domains requiring sequential decision-making under uncertainty, including robotics, recommendation systems, and autonomous control systems. While specific grant information is not provided in the available materials, his publications in top venues suggest successful research funding.

Dr. Peled-Cohen is actively involved in both academic and industrial research, contributing to the advancement of machine learning theory while maintaining strong connections with practical applications through his position at Google Research.