Event Category: Operations Research Seminar Series
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Online Optimization and Learning under Long-Term Convex Constraints and Objective
4/27/2017 | 4:15pm | E51-335 Reception to follow. Shipra Agrawal Assistant Professor Columbia University Abstract: Sequential decision making situations in real world applications often involve multiple long term constraints and nonlinear objectives. Some examples from online advertising include budget constraints, nonlinear under-delivery penalties, need for diversity in allocation, and managing risk…
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New Matching Methods for Causal Inference and Impact Evaluation using Mathematical Programming
4/20/2017 | 4:15pm | E51-335 Reception to follow. Jose Zubizarreta Assistant Professor Columbia University Abstract: In observational studies of causal effects, matching methods are often used to approximate the ideal study that would be conducted if it were possible to do it by controlled experimentation. In this talk, I will discuss new…
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Predicting Travel Time Reliability on Large-Scale Road Networks
4/13/2017 | 4:15pm | E51-335 Reception to follow. Dawn Woodard Senior Data Science Manager of Dynamic Pricing Uber Abstract: Estimates of road speeds have become commonplace and central to route planning, but few systems in production provide information about the reliability of the prediction. Probabilistic forecasts of travel time capture reliability, and can…
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JuMP: A Modeling Language for Mathematical Optimization
3/23/2017 | 4:15pm | E51-325 Reception to follow. Miles Lubin and Joseph Huchette ORC Doctoral Students MIT Abstract: JuMP is an open-source modeling language that allows users to express a wide range of optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and nonlinear) in a high-level, algebraic syntax. JuMP takes advantage of advanced features…
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Optimal Contract to Induce Continued Effort
3/16/2017 | 4:15pm | E51-335 Reception to follow. Peng Sun Professor Duke University Abstract: We consider a basic model of a risk neutral principal incentivizing a risk neutral agent to exert effort in order to raise the arrival rate of a Poisson process. The effort is costly to the agent, unobservable to…
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Data-driven Methods to Improve Kidney Allocation and other Resource Allocation Systems
3/9/2017 | 4:15pm | E51-335 Reception to follow. Nikolaos Trichakis Assistant Professor MIT Abstract: In this paper we study systems that allocate different types of scarce resources to heterogeneous allocatees based on predetermined priority rules, e.g., the U.S. deceased-donor kidney allocation system or the public housing program. We tackle the problem of…
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Fully Polynomial-time Approximation Schemes for Continuous Stochastic Dynamic Programs: Theory and Applications
3/2/2017 | 4:15pm | E51-335 Reception to follow. Giacomo Nannicini Research Staff Member IBM Abstract: We study the problem of approximating the optimal value and the optimal policy of a stochastic dynamic program with continuous state and action spaces. This problem is harder than its discrete counterpart and does not admit…
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Facilitating the Search for Partners on Matching Platforms: Restricting Agent Actions
2/23/2017 | 4:15pm | E51-335 Reception to follow. Yash Kanoria Assistant Professor Columbia University Abstract: Two-sided matching platforms, such as those for labor, accommodation, dating, and taxi hailing, control many aspects of the search for partners. We consider a dynamic model of search with costly discovery of match value and find that…
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Random Number Generation with Multiple Streams for Sequential and Parallel Computing
2/16/2017 | 4:15pm | E51-335 Reception to follow. Pierre L’Ecuyer Canada Research Chair in Stochastic Simulation and Optimization University of Montreal Abstract: We provide a review of the state of the art on the design and implementation of random number generators for simulation, on both sequential and parallel computing environments. We focus…
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Learning to Personalize from Observational and Behavioral Data
RESCHEDULED DUE TO SNOW 2/10/2017 | 12:00pm | E62-350 Reception to follow. Nathan Kallus Assistant Professor Cornell University Abstract: Personalization has long been central in machine learning, with successful applications to recommendation systems in electronic settings. A question of growing urgency is how to translate this success to emergent contexts in medicine and business…