معرفی
Alex Olshevsky is an Associate Professor of Electrical Engineering at Boston University. He holds affiliations with the Department of Electrical and Computer Engineering, the Division of Systems Engineering, and the Department of Computer Science (as an affiliate). His research focuses on distributed optimization, machine learning, control of networks, multi-agent systems, and reinforcement learning. He earned his BS from Georgia Tech and MS/PhD from MIT.
Education:
- BS, Georgia Institute of Technology
- MS, Massachusetts Institute of Technology
- PhD, Massachusetts Institute of Technology
Research interests emphasize theoretical foundations and applications of distributed algorithms, with a focus on optimization, learning, and control in networked systems. Key areas include reinforcement learning for control systems, distributed machine learning, and network-based epidemic control. His work bridges systems theory, optimization, and machine learning to address challenges in multi-agent coordination and large-scale data-driven systems.
Notable contributions include advances in distributed stochastic gradient methods, temporal difference learning, and adversarial crowdsourcing. His research has been published in top venues like IEEE Transactions on Automatic Control, Journal of Machine Learning Research, and NeurIPS/ICML.
Advising and grants include mentoring students now at institutions like Hong Kong University of Science and Technology and LinkedIn. His grants include NSF-funded projects on federated learning for energy consumption prediction and reinforcement learning algorithm design.
Current roles include leadership in Boston University's systems engineering and computer science programs. He co-founded read.haus, leveraging machine learning for personalized book recommendations.
