Amin Saberi

About

Amin Saberi is a Professor of Management Science and Engineering at Stanford University and also holds a courtesy appointment in Computer Science. His research focuses on management science, engineering, and computer science, contributing to the understanding and development of these fields. As a faculty member at Stanford, he is involved in advancing knowledge through teaching and research, although specific details of his research interests and key contributions are not provided in the page text.

Research topics

• Computer Science
• Artificial Intelligence
• Operations research
• Internal medicine
• Medicine
• Statistics
• Mathematics
• Machine Learning
• Mathematical optimization
• Finance

Selected publications

• Near-Optimal Bayesian Online Assortment of Reusable Resources

  Proceedings of the 23rd ACM Conference on Economics and Computation · 2022 · 21 citations

  Senior authorCorresponding

  Motivated by the applications of rental services in e-commerce, we consider revenue maximization in online assortment of reusable resources for a stream of arriving consumers with different types. We design competitive online algorithms with respect to the optimum online policy in the Bayesian setting, in which types are drawn independently from known heterogeneous distributions over time. In the regime where the minimum of initial inventories c_min is large, our main result is a near-optimal 1-…

  DOI

• Two-stage Stochastic Matching with Application to Ride Hailing

  Society for Industrial and Applied Mathematics eBooks · 2021 · 12 citations

  Senior authorCorresponding

  We study a two-stage stochastic matching problem motivated in part by applications in online marketplaces used for ride hailing. Using a randomized primal-dual algorithm applied to a family of “balancing” convex programs, we obtain the optimal 3/4 competitive ratio against the optimum offline benchmark. These balancing convex programs offer a natural generalization of the matching skeleton by Goel et al. (2012) and may be of independent interest. Switching to the more precise benchmark of optimu…

  DOI

• The Value of Excess Supply in Spatial Matching Markets

  Proceedings of the 23rd ACM Conference on Economics and Computation · 2022 · 11 citations

  Senior authorCorresponding

  We study dynamic matching in a spatial setting. Drivers are distributed at random on some interval. Riders arrive in some (possibly adversarial) order at randomly drawn points. The platform observes the location of the drivers and can match newly arrived riders immediately or can wait for more riders to arrive. Unmatched riders incur a waiting cost of c per period. Furthermore, the platform can match riders and drivers irrevocably, and the cost of matching a driver to a rider is equal to the dis…

  PublisherDOI

• New Philosopher Inequalities for Online Bayesian Matching, via Pivotal Sampling

  Society for Industrial and Applied Mathematics eBooks · 2025-01-01 · 3 citations

  book-chapter

  We study the polynomial-time approximability of the optimal online stochastic bipartite matching algorithm, initiated by Papadimitriou et al. (EC’21). Here, nodes on one side of the graph are given upfront, while at each time t, an online node and its edge weights are drawn from a time-dependent distribution. The optimal algorithm is PSPACE-hard to approximate within some universal constant. We refer to this optimal algorithm, which requires time to think (compute), as a philosopher, and refer t…

  PublisherDOI

• A Local Graph Limits Perspective on Sampling-Based GNNs

  2025-06-22 · 2 citations

  articleSenior author

  We offer a novel theoretical perspective on employing sub graph sampling methods for the training of graph neural networks (GNNs). We prove that, under mild assumptions, parameters learned from training GNNs on small samples of a large input graph are within an ∊-neighborhood of the outcome of training the same architecture on the entire graph. We derive bounds on the number of samples, the size of the sub graph, and the training steps required as a function of ∊. Our results offer a theoretical…

  PublisherDOI

Recent grants

• CAREER: Algorithms for Markets, Games and their Applications

  NSF · $400k · 2006–2013

• AF: Small: Rounding by Sampling Method and Applications to Traveling Salesman Problems

  NSF · $500k · 2012–2017

• AF: Small: Geometry of Polynomials and Algorithm Design

  NSF · $500k · 2018–2023

Frequent coauthors

• Simon B. Eickhoff

  Heinrich Heine University Düsseldorf

  62 shared

• Masoud Tahmasian

  45 shared

• Sofie L. Valk

  Heinrich Heine University Düsseldorf

  43 shared

• Boris C. Bernhardt

  Montreal Neurological Institute and Hospital

  25 shared

• Jean‐Luc Martinot

  Inserm

  25 shared

• Éric Artiges

  Centre National de la Recherche Scientifique

  25 shared

• Meike D. Hettwer

  Heinrich Heine University Düsseldorf

  24 shared

• Ali Shameli

  Stanford University

  22 shared

Education

• Ph.D., Management Science and Engineering

  Stanford University

  2000

• M.S., Management Science and Engineering

  Stanford University

  1995

• B.S., Electrical Engineering

  University of Tehran

  1990

Awards & honors

• Terman Fellowship
• Alfred Sloan Fellowship
• 2025 ACM SIGecom Test of Time Award
• ACM SIGecom Test of Time Award (2024)

Similar researchers at Stanford University

• Ashish Goelh-52
• Benjamin Van Royh-51
• Renyuan Xuh-51