Research
Optimization is concerned with the analysis and algorithmic aspects of maximizing or minimizing an objective function subject to constraints, often in complex problems in high dimension. Active research areas in ORFE include interior-point methods, the parametric simplex method, stochastic optimization, and convex analysis. Applications of interest span from portfolio optimization to engineering applications such as optimal control, optical design, and machine learning.
Faculty Research Interests
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multi-armed bandits, online optimization, stochastic optimization, statistical learning theory, high-dimensional statistics
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security pricing, stochastic processes, stochastic modeling, mathematical finance, risk analysis
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dynamic resource management, traveler information and decision support, automated vehicle control systems, traveler information systems, personal rapid transit
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approximate dynamic programming and optimal learning, with applications in energy, homeland security, health and complex resource allocation problems
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nonparametric statistics, statistical learning theory, high dimensional statistics, bandit problems, aggregation, stochastic optimization, dimension reduction
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financial management and risk analysis, hedging in incomplete markets, convex and coherent risk measures, convex analysis, mathematical finance, risk management
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convex optimization, interior-point methods, efficient frontiers associated with L¹ risk measures, optimal stopping and related differential equations, high-contrast imaging, celestial mechanics, stability of Saturn's rings, political mapping
© 2011 The Trustees of Princeton University • Department of Operations Research and Financial Engineering • Princeton, NJ 08544