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
• Computer network
• Business
• Operations research
• Advertising
• Marketing

Selected publications

• Position Auctions in AI-Generated Content

  2026-04-12

  articleOpen access1st authorCorresponding

  We consider an extension to classic position auctions in which sponsored creatives are embedded within AI-generated content rather than shown in predefined slots. Leveraging advanced LLM technologies, it becomes viable to seamlessly integrate sponsored creatives with AI content and accurately estimate the context-aware benefits of differing insertion positions. However, this approach introduces novel challenges; substitution effects require rigorous treatment compared to standard position auction settings, where slots are independent of each other.

  PublisherDOI

• Regularized Online Allocation Problems: Fairness and Beyond

  Manufacturing & Service Operations Management · 2025-02-20 · 12 citations

  article1st authorCorresponding

  Problem definition: Online allocation problems with resource constraints have a rich history in operations management. In this paper, we introduce the regularized online allocation problem, a variant that includes a nonlinear regularizer acting on the total resource consumption. In this problem, requests repeatedly arrive over time, and for each request, a decision-maker needs to take an action that generates a reward and consumes resources. The objective is to simultaneously maximize additively separable rewards and the value of a non-separable regularizer subject to the resource constraints. Methodology/results: We design an algorithm that is simple and fast and attains good performance with stochastic and adversarial inputs. In particular, our algorithm is asymptotically optimal under stochastic i.i.d. input models, attains a fixed competitive ratio that depends on the regularizer when the input is adversarial, and can handle a sublinear amount of non-stationarity. Furthermore, the algorithm and analysis do not require convexity or concavity of the reward function and the consumption function, which allows more model flexibility. Numerical experiments confirm the effectiveness of the proposed algorithm and of regularization in an Internet advertising application. Managerial implications: Introducing a regularizer allows decision-makers to trade off separable objectives such as the economic efficiency of an allocation with ancillary, non-separable objectives such as fairness or equity of an allocation. Our results have implications for online allocation problems across many sectors, such as Internet advertising, cloud computing, and humanitarian logistics, in which fairness and equity are key considerations for managers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0212 .

  PublisherDOI

• Distributed Load Balancing with Workload-Dependent Service Rates

  2025-07-02

  articleOpen access

  In many real-world applications such as data centers and cloud computing, the systems often consist of multiple frontends (routers) that receive job requests and backends (servers) that process these jobs. Efficient resource management is becoming increasingly important given the growing demand for serving machine learning inference queries, which incur high latencies and require expensive computational resources.

  PublisherOA PDFDOI

• Single-Leg Revenue Management with Advice

  Operations Research · 2025-10-09 · 1 citations

  article1st authorCorresponding

  Machine learning algorithms are becoming increasingly powerful, but with that power comes greater complexity and opacity. As these models become more sophisticated, they become increasingly difficult to understand—and, crucially, harder to anticipate when and how they might fail. This makes it essential to incorporate their predictions in ways that remain robust to errors. In “Single-Leg Revenue Management with Advice,” S. Balseiro, C. Kroer, and R. Kumar develop an algorithm for the classical single-leg revenue management problem that robustly incorporates predictions. They uncover a fundamental tradeoff: Placing greater trust in predictive models can yield high performance when predictions are accurate but also makes algorithms vulnerable when predictions are off. The proposed algorithm achieves the optimal tradeoff between these goals, allowing decision makers to leverage machine learning predictions while guarding against their potential inaccuracies. By doing so, this work provides a principled approach to integrating powerful yet imperfect forecasts into real-world decision making.

  PublisherDOI

• Battery Operations in Electricity Markets: Strategic Behavior and Distortions

  2025-10-29

  article
  PublisherDOI

• Battery Operations in Electricity Markets: Strategic Behavior and Distortions

  2025-07-02

  articleOpen access

  Electric power systems are undergoing a major transformation as they integrate intermittent renewable energy sources, and batteries to smooth out variations in renewable energy production. As privately-owned batteries grow from their role as marginal "price-takers" to significant players in the market, a natural question arises: How do batteries operate in electricity markets, and how does the strategic behavior of decentralized batteries distort decisions compared to centralized batteries? We propose an analytically tractable model that captures salient features of the highly complex electricity market. We derive in closed form the resulting battery behavior and generation cost in three operating regimes: (i) no battery, (ii) centralized battery, and (ii) decentralized profit-maximizing battery. We establish that a decentralized battery distorts its discharge decisions in three ways. First, there is quantity withholding, i.e., discharging less than centrally optimal. Second, there is a shift in participation from day-ahead to real-time, i.e., postponing some of its discharge from day-ahead to real-time. Third, there is reduction in real-time responsiveness, or discharging less in response to smoothing real-time demand than centrally optimal. We also quantify the impact of the battery market power on total system cost via the Price of Anarchy metric, and prove that it is always between 9/8 and 4/3. That is, incentive misalignment always exists, but it is bounded even in the worst case. We calibrate our model to real data from Los Angeles and Houston. Lastly, we show that competition is very effective at reducing distortions, but many market power mitigation mechanisms backfire, and lead to higher total cost. The work provides stakeholders with a framework to understand and detect market power from batteries. It also shows that the potential loss from battery market power is relatively small compared to the cost reduction achievable from having enough battery capacity in the system. Therefore, independent system operators in rapidly changing markets might want to prioritize market entry of batteries and only shift to market power mitigation once the market is more mature.

  PublisherOA PDFDOI

• Load Balancing with Network Latencies via Distributed Gradient Descent

  ArXiv.org · 2025-04-14

  preprintOpen access1st authorCorresponding

  Motivated by the growing demand for serving large language model inference requests, we study distributed load balancing for global serving systems with network latencies. We consider a fluid model in which continuous flows of requests arrive at different frontends and need to be routed to distant backends for processing whose processing rates are workload dependent. Network latencies can lead to long travel times for requests and delayed feedback from backends. The objective is to minimize the average latency of requests, composed of the network latency and the serving latency at the backends. We introduce Distributed Gradient Descent Load Balancing (DGD-LB), a probabilistic routing algorithm in which each frontend adjusts the routing probabilities dynamically using gradient descent. Our algorithm is distributed: there is no coordination between frontends, except by observing the delayed impact other frontends have on shared backends. The algorithm uses an approximate gradient that measures the marginal impact of an additional request evaluated at a delayed system state. Equilibrium points of our algorithm minimize the centralized optimal average latencies, and we provide a novel local stability analysis showing that our algorithm converges to an optimal solution when started sufficiently close to that point. Moreover, we present sufficient conditions on the step-size of gradient descent that guarantee convergence in the presence of network latencies. Numerical experiments show that our algorithm is globally stable and optimal, confirm our stability conditions are nearly tight, and demonstrate that DGD-LB can lead to substantial gains relative to other load balancers studied in the literature when network latencies are large.

  PublisherOA PDFDOI

• Dynamic Pricing for Reusable Resources: The Power of Two Prices

  Operations Research · 2025-08-21 · 1 citations

  article1st authorCorresponding

  Two Prices Unlock Big Gains for Reusable Resources How much sophistication is needed to price reusable resources, like hotel rooms and cloud computing, when usage durations are not memoryless? Surprisingly little. In “Dynamic Pricing for Reusable Resources: The Power of Two Prices,” Balseiro, Ma, and Zhang propose a class of dynamic stock-dependent policies that achieve significant improvements over static pricing by only looking at how many units are busy and ignoring how long they have been busy. Using an “insensitivity” property of loss networks, they show that optimizing within this policy class can be formulated as a tractable convex optimization problem. Better yet, the performance loss of the optimal stock-dependent policy can be achieved by a simple two-price policy: charge a high price when inventory falls below a threshold and a low price otherwise. Extensions to multiple resources and customer classes, together with extensive simulations, confirm that “just a little” dynamicity can go a long way.

  PublisherDOI

• On the Robustness of Second-Price Auctions in Prior-Independent Mechanism Design

  Operations Research · 2024-05-16 · 4 citations

  article

  Market designers often only have partial information about the environment and prefer simple mechanisms that are robust to the underlying uncertainty. In “On the Robustness of Second-Price Auctions in Prior-Independent Mechanism Design,” Anunrojwong, Balseiro, and Besbes study the fundamental problem of selling an item to n buyers, when only an upper bound on values is known and the seller minimizes worst-case regret. They establish that the same mechanism is robustly optimal across a wide range of environments for distributions of values: i.i.d., mixtures of i.i.d., exchangeable, and affiliated (the last two capturing positive dependence). Moreover, this robust mechanism is a second-price auction with random reserve price depending on n. Without positive dependence, the problem reduces to the one-buyer case. This result supports the wide use of second-price auctions in practice and allows them to quantify the robust value of competition in auctions.

  PublisherDOI

• Optimal Mechanisms for a Value Maximizer: The Futility of Screening Targets

  2024-07-08

  article1st authorCorresponding

  Motivated by the increased adoption of autobidding algorithms in internet advertising markets, we study the design of optimal mechanisms for selling an item to a value-maximizing buyer with a return-on-spend constraint. The buyer's values and target ratio in the return-on-spend constraint are private. We restrict attention to deterministic sequential screening mechanisms that can be implemented as a menu of two-part tariffs. The main result of this paper is to provide a characterization of an optimal mechanism. Surprisingly, we show that the optimal mechanism does not require target screening, i.e., offering a single two-part tariff is optimal for the seller. The optimal mechanism is a subsidized two-part tariff that provides a lump-sum subsidy to the buyer to encourage participation and then charges a fixed unit price for each item sold. The seller's problem is a challenging non-linear mechanism design problem, and a key technical contribution of our work is to provide a novel approach to analyzing non-linear pricing contracts for constrained buyers. Our results have valuable implications for advertising platforms seeking to personalize pricing decisions based on advertisers' characteristics.

  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.