About

Abhinav Madabhushi is a Ph.D. student in Operations Research at Georgia Tech, advised by Professor Katya Scheinberg. His research interests are in continuous optimization, with a focus on comparison-based optimization, particularly for settings where standard optimization information is unavailable or expensive to obtain. He is interested in developing theoretically grounded algorithms that remain effective in practical applications, including machine learning and reinforcement learning. He is supported by the Thomas H. Johnson Fellowship.

Before joining Georgia Tech, he received a B.S. in Data Theory with a minor in Data Science Engineering from UCLA. His prior work includes learning-to-optimize methods for interior-point methods, reinforcement learning, and applications of machine learning to sports analytics.