Machine Learning

Machine Learning

Faculty Research Interests

dynamic pricing, revenue management, machine learning, logistics, supply chain management, algorithmic trading, statistical arbitrage, traffic flow modeling, and transportation analysis

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

algorithmic/program trading, machine learning/statistical modeling, computational/mathematical finance, statistical signal processing, optimization