Machine Learning

Machine Learning

Faculty Research Interests

algorithms for linear, quadratic, semidefinite, convex and general nonlinear programming, network flows, large sparse systems, and applications in robust optimization, finance, and imaging

convex optimization, robust optimization, combinatorial optimization, computational finance, complex systems, systemic risk, information theory

optimization and machine learning, including data-driven optimization under partial, uncertain, and online inputs, and related concepts in learning, namely multi-armed bandits, online learning, and reinforcement learning


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