My research investigates how to make complex, high-dimensional decision problems computationally tractable while uncovering structural insights that guide effective decision-making. I develop optimization and approximation methodologies that bridge mathematical programming, stochastic control, and artificial intelligence. A central theme of my work is exploiting problem structure, such as weak coupling and network interactions, to design scalable algorithms with provable guarantees and strong numerical performance. These methods enable rigorous and practical decision support in scheduling, transportation, and healthcare systems characterized by uncertainty and combinatorial complexity.

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