About

Hamed Khosravi is a Machine Learning Ph.D. student in Industrial and Systems Engineering at Georgia Tech, advised by Professor Xiaoming Huo. His research interests include statistical machine learning, reinforcement learning, statistical evaluation of AI systems, and multi-armed bandit theory. His current work focuses on developing statistically principled methods for evaluating and improving AI systems, including large language models and sequential decision-making systems.

Before joining Georgia Tech, Hamed received his M.S. in Industrial Engineering from West Virginia University and his B.S. in Industrial Engineering from Iran University of Science and Technology. His work spans applications of machine learning and optimization in smart manufacturing, healthcare, and human-AI decision making. He also has industry experience as a Machine Learning Engineer Intern at Tesla.

Hamed regularly presents his research at INFORMS and IISE annual meetings and has received recognition in several competitive research and data challenges. His honors include the Most Innovative Approach Award in the NSF Future Manufacturing Data Challenge, finalist recognition in the INFORMS QSR Data Challenge, third place in an INFORMS poster competition, recognition in the IISE Energy Analytics Data Challenge, two NSF travel awards, and university fellowships.