Optimization

Optimization

Faculty Research Interests

combinatorial optimization and integer programming, computational modeling of power grids

discrete Optimization, market design, scheduling, applied probability

graph theory and combinatorial optimization

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

combinatorial optimization, scheduling, green computing, network and internet algorithm, development of efficient algorithms for computationally hard problems with both provable guarantees and practical impact 

dynamic pricing, discrete choice modeling, assortment optimization, design and pricing of bundles, real options

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

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

stochastic systems and applied probability, resource control in stochastic networks, financial systemic risk, risk hedging in production systems, healthcare operations, hospital resource planning, supply chain optimization

logistics, optimization, pricing and revenue management, and supply chain management