Mostly OM

Prof. Santiago Balseiro

Prof. Santiago BalseiroColumbia UniversityTalk: Dynamic Mechanisms with Martingale UtilitiesAbstract:We study the dynamic mechanism design problem of a seller who repeatedly sells independent items to a buyer with private values. In this setting, the seller could potentially extract the entire buyer surplus by running efficient auctions and charging an upfront participation fee at the beginning...

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Prof. Jing Dong

Prof. Jing DongColumbia UniversityTalk: On Modeling Patient Flow Dynamics in Inpatient UnitsAbstract:Hospital-related queues have unique features that are not captured by standard queueing assumptions, necessitating the development of specialized models. In this paper, we propose a queueing model that takes into account the most salient features of queues associated with patient-flow dynamics i...

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Prof. Yonatan Gur

Prof. Yonatan GurStanford UniversityTalk: Learning in Repeated Auctions with Budgets: Regret Minimization and EquilibriumAbstract:In online advertising markets, advertisers often purchase ad placements through bidding in repeated auctions based on realized viewer information. We study how budget-constrained advertisers may compete in such sequential auctions in the presence of uncertainty about...

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Prof. Ming Hu

Prof. Ming HuUniversity of TorontoTalk: A Theory Unifying The Long Tail and Blockbuster PhenomenaAbstract:We provide a theory that unifies the long tail and blockbuster phenomena. Specifically, we analyze a three-stage game where, first, a large number of potential firms make entry decisions, then those who stay in the market decide on the investment in its product, and lastly customers with he...

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Prof. Macro Scarsini

Prof. Macro ScarsiniLibera Università Internazionale degli Studi Sociali Guido CarliTalk: Social Learning from Online Reviews with Product ChoiceAbstract:This paper studies product ranking mechanisms of a monopolistic online platform in the presence of social learning. The products' quality is initially unknown, but consumers can sequentially learn it as online reviews accumulate. A salient asp...

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Prof. Cong Shi

Prof. Cong ShiUniversity of MichiganTalk: Closing the Gap: A Learning Algorithm for Lost-Sales Inventory Systems with Lead TimesAbstract:We consider a periodic-review, single-product inventory system with lost sales and positive lead times under censored demand. In contrast to the classical inventory literature, we assume the firm does not know the demand distribution a priori and makes an adap...

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Prof. David Simchi-Levi

Prof. David Simchi-LeviMassachusetts Institute of TechnologyTalk: Online Resource Allocation with Applications to Revenue ManagementAbstract:Online resource allocation is a fundamental problem in OR and CS with applications such as offering products to customers, distributing jobs to candidates, assigning advertisers to ad slots, and matching drivers to passengers. These problems can be abstrac...

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Prof. Mengdi Wang

Prof. Mengdi WangPrinceton UniversityTalk: On the (Reduced) Complexity of Markov Decision ProcessAbstract:The Markov decision problem (MDP) is one of the most basic models for sequential decision-making problems in a dynamic environment where outcomes are partly random. It models a stochastic control process in which a planner makes a sequence of decisions as the system evolves. The Markov deci...

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Prof. Tong Wang

Prof. Tong WangNational University of SingaporeTalk: Forecasting Demand for New Products: A Hybrid Structural & Data-Driven ApproachAbstract:We study a new product demand forecasting problem faced by a cosmetic retailer. The retailer’s business strategy requires frequent launching of new products with new functionalities (e.g., facial mask) or new regimes (e.g., Tea Tree Oil). The main challen...

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Prof. Yao Xie

Prof. Yao XieGeorgia Institute of TechnologyTalk: Active and Robust Recommender System

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Prof. Dan Zhang

Prof. Dan ZhangUniversity of ColoradoTalk: Finite-Horizon Approximate Linear Programs for an Infinite-Horizon Revenue Management ProblemAbstract:Approximate linear programs (ALPs) have been used extensively to approximately solve stochastic dynamic programs that suffer from the well-known curse of dimensionality. Due to canonical results establishing the optimality of stationary value functions...

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Prof. Assaf Zeevi

Prof. Assaf ZeeviColumbia UniversityTalk: Learning and Earning: An (abridged and biased) Guided Tou

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