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Hassan is an Operations Research Ph.D. candidate in the H. Milton Stewart School of Industrial & Systems Engineering at Georgia Tech, minoring in Machine Learning. His research is primarily focused on exploiting (combinatorial) structures present in optimization and machine learning problems to (i) develop novel and efficient algorithms with provable theoretical guarantees; (ii) speed-up existing methods and draw connections between them. In particular, his work integrates the theory of approximation algorithms, combinatorial optimization and submodular functions, iterative first-order optimization methods and smarter warm-start solutions.
Hassan interned as an Applied Scientist at Amazon during the Summer of 2022. Before this, he obtained his MS in Management Science and Engineering from Columbia University. During his time at Columbia, he worked as a part-time analytics consultant for several companies. In his Master’s Thesis he worked on the Assortment Optimization problem from a risk perspective, where the objective was minimizing the risk that rises from the randomness of peoples' choices. He developed exact algorithms and optimization models to determine an optimal risk-averse assortment that does not encounter large revenue deviations.
He is a recipient of the Stewart, McLean, and the ARC TRIAD Fellowship awards.