Alexander Levine

Alexander Levine serves as a Postdoctoral Research Fellow at the Institute for Foundations of Machine Learning, The University of Texas at Austin, advised by Professors Amy Zhang (Department of Electrical and Computer Engineering) and Peter Stone (Department of Computer Science).

His academic background includes a Ph.D. from the University of Maryland (2023), where his dissertation focused on adversarial robustness in supervised deep learning.

Levine's research centers on Artificial Intelligence and Machine Learning, with specialized expertise in Reinforcement Learning. His current work investigates deep representation learning for control tasks, robust reinforcement learning, and goal-conditioned reinforcement learning, building upon his doctoral contributions to classification stability under adversarial perturbations. This research portfolio demonstrates consistent focus on enhancing reliability in learning systems across both supervised and reinforcement paradigms.

As an active member of the Institute for Foundations of Machine Learning, Levine contributes to interdisciplinary efforts advancing theoretical frameworks in machine learning, particularly in control-oriented applications requiring robust performance guarantees.