Santiago Balseiro

About

Santiago R. Balseiro is the George E. Warren Professor of Business at the Graduate School of Business, Columbia University, and a research scientist at Google Research. His research develops novel methodological approaches that combine dynamic optimization, stochastic modeling, and game theory to address fundamental problems in the digital economy. His work tackles central problems in internet advertising while making methodological contributions to the area of large-scale sequential decision-making in the face of uncertainty and dynamic optimization with incentives. Balseiro's research has been recognized by multiple awards including an early career award, a best dissertation award, and numerous best paper awards. He is also the Research Director of the Deming Center. Balseiro is a graduate of the University of Buenos Aires and received his Ph.D. from Columbia University’s Graduate School of Business in 2013. Before joining Columbia, he was on the faculty at the Fuqua School of Business, Duke University.

Research topics

• Computer Science
• Mathematical optimization
• Artificial Intelligence
• Political Science
• Microeconomics
• Economics
• Mathematics
• Algorithm
• World Wide Web
• Applied mathematics

Selected publications

• Online Display Advertising Markets: A Literature Review and Future Directions

  Information Systems Research · 2020 · 178 citations

  Display advertising is a $50 billion industry in which advertisers’ (e.g., P&G, Geico) demand for impressions is matched to publishers’ (e.g., Facebook, Wall Street Journal) supply of them. An ideal match is one wherein the publisher’s ad impression is assigned to the advertiser with the highest value for it. Intermediaries (e.g., Google) facilitate this match between advertisers and publishers by managing data and providing optimization tools and algorithms for serving ads. Although these m…

  DOI

• The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems

  Operations Research · 2022 · 65 citations

  1st authorCorresponding

  A Novel Class of Robust and Fast Algorithms for Online Allocation Problems A central problem in operations research is allocating limited resources sequentially to maximize cumulative rewards. Applications abound and include network revenue management and internet advertising among many others. Existing data-driven algorithms are tailored for convex settings with either adversarial or stochastic inputs. Many modern applications of online allocations problems, however, are nonconvex. Furthermore,…

  DOI

• Dynamic Pricing of Relocating Resources in Large Networks

  Management Science · 2020 · 57 citations

  1st authorCorresponding

  Motivated by applications in shared vehicle systems, we study dynamic pricing of resources that relocate over a network of locations. Customers with private willingness to pay sequentially request to relocate a resource from one location to another, and a revenue-maximizing service provider sets a price for each request. This problem can be formulated as an infinite-horizon stochastic dynamic program, but it is difficult to solve, as optimal pricing policies may depend on the locations of all re…

  PublisherDOI

• Survey of Dynamic Resource-Constrained Reward Collection Problems: Unified Model and Analysis

  Operations Research · 2023-05-09 · 33 citations

  article1st authorCorresponding

  Dynamic resource allocation problems arise under a variety of settings. In “Survey of Dynamic Resource-Constrained Reward Collection Problems: Unified Model and Analysis,” Balseiro, Besbes, and Pizarro introduce a unifying model for a large class of dynamic optimization problems dubbed dynamic resource-constrained reward collection (DRC 2 ) problems. Surveying the literature, they show that this class encompasses a variety of disparate and classical problems typically studied separately, such as…

  PublisherDOI

• Contextual Standard Auctions with Budgets: Revenue Equivalence and Efficiency Guarantees

  Management Science · 2023-04-06 · 13 citations

  article1st authorCorresponding

  The internet advertising market is a multibillion dollar industry in which advertisers buy thousands of ad placements every day by repeatedly participating in auctions. An important and ubiquitous feature of these auctions is the presence of campaign budgets, which specify the maximum amount the advertisers are willing to pay over a specified time period. In this paper, we present a new model to study the equilibrium bidding strategies in standard auctions, a large class of auctions that include…

  PublisherDOI

Frequent coauthors

• Vahab Mirrokni

  57 shared

• Song Zuo

  PLA Army Service Academy

  26 shared

• Omar Besbes

  Columbia University

  25 shared

• Renato Paes Leme

  15 shared

• Jieming Mao

  15 shared

• Yuan Deng

  Guizhou Electric Power Design and Research Institute

  13 shared

• Jerry Anunrojwong

  Columbia University

  12 shared

• Haihao Lu

  12 shared

Labs

• Santiago R. Balseiro LabPI

  Develops novel methodological approaches that combine dynamic optimization, stochastic modeling, and game theory to address fundamental problems in the digital economy.

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