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
• Pathology
• World Wide Web
• Algorithm
• Pharmacology
• Physical therapy
• Geography
• Engineering
• Business

Selected publications

• Stochastic Online Metric Matching: Adversarial Is No Harder Than Stochastic

  Lecture notes in computer science · 2026-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• 340. Common Cortical Patterns Shape Variability Within and Overlap Across Psychiatric Disorders – A Transdiagnostic ENIGMA Study

  Biological Psychiatry · 2026-04-25

  article
  PublisherDOI

• Optimal Rounding for Two-Stage Bipartite Matching

  Society for Industrial and Applied Mathematics eBooks · 2026-01-01

  book-chapter

  We study two-stage bipartite matching, in which the edges of a bipartite graph on vertices \((B_1 \cup B_2, I)\) are revealed in two batches. In stage one, a matching must be selected from among revealed edges \(E \subseteq B_1 \times I\). In stage two, edges \(E^\theta \subseteq B_2 \times I\) are sampled from a known distribution, and a second matching must be selected between \(B_2\) and unmatched vertices in \(I\). The objective is to maximize the total weight of the combined matching. We design polynomial-time approximations to the optimum online algorithm, achieving guarantees of \(^{{7}}/_{{8}}\) for vertex-weighted graphs and \(2\sqrt{2}-2 \approx 0.828\) for edge-weighted graphs under arbitrary distributions. Both approximation ratios match known upper bounds on the integrality gap of the natural fractional relaxation, improving upon the best-known approximation of \(0.767\) by Feng, Niazadeh, and Saberi for unweighted graphs whose second batch consists of independently arriving nodes.

  PublisherDOI

• amnsbr/cubnm_paper: v0.2.0

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-24

  otherOpen access1st authorCorresponding

  Including code associated with the preprint v2 of Saberi et al., "cuBNM: GPU-Accelerated Brain Network Modeling"

  PublisherDOI

• Adolescent maturation of cortical excitation-inhibition ratio based on individualized biophysical network modeling

  Science Advances · 2025-06-06 · 11 citations

  articleOpen access1st authorCorresponding

  The excitation-inhibition ratio is a key functional property of cortical microcircuits which changes throughout an individual’s lifespan. Adolescence is considered a critical period for maturation of excitation-inhibition ratio. This has primarily been observed in animal studies. However, there is limited human in vivo evidence for maturation of excitation-inhibition ratio at the individual level. Here, we developed an individualized in vivo marker of regional excitation-inhibition ratio in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional imaging data from both cross-sectional ( n = 752) and longitudinal ( n = 149) cohorts. In both datasets, we found a widespread decrease in excitation-inhibition ratio in association areas, paralleled by an increase or lack of change in sensorimotor areas. This developmental pattern was aligned with multiscale markers of sensorimotor-association differentiation. Although our main findings were robust across alternative modeling configurations, we observed local variations, highlighting the importance of methodological choices for future studies.

  PublisherDOI

• Resting-state Regional Cerebral Blood Flow Alterations in Macrovasculature and Microvasculature in Parkinson’s Disease: A Systematic Review and Meta-analysis (P3-5.013)

  Neurology · 2025-04-07

  review

  To assess changes in resting-state cerebral blood flow (CBF) across various brain regions and CBF velocity (CBFv) of the middle cerebral artery (MCA) in Parkinson’s disease (PD) compared to healthy controls (HC).

  PublisherDOI

• Is Cerebral Blood Flow Altered in Parkinson’s Disease? A Systematic Review and Meta-analysis (P3-5.023)

  Neurology · 2025-04-07

  review

  To meta-analytically determine how resting-state cerebral blood flow (CBF) changes in Parkinson’s disease (PD) compared to healthy controls (HC).

  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 to polynomial-time online approximations of the above as philosopher inequalities. The best known philosopher inequality for online matching yields a 0.652-approximation. In contrast, the best possible prophet inequality, or approximation of the optimum offline solution, is 0.5.

  PublisherDOI

• StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework

  ArXiv.org · 2025-06-17

  preprintOpen accessSenior author

  Every individual carries a unique and personal life story shaped by their memories and experiences. However, these memories are often scattered and difficult to organize into a coherent narrative, a challenge that defines the task of autobiography writing. Existing conversational writing assistants tend to rely on generic user interactions and pre-defined guidelines, making it difficult for these systems to capture personal memories and develop a complete biography over time. We introduce StorySage, a user-driven software system designed to meet the needs of a diverse group of users that supports a flexible conversation and a structured approach to autobiography writing. Powered by a multi-agent framework composed of an Interviewer, Session Scribe, Planner, Section Writer, and Session Coordinator, our system iteratively collects user memories, updates their autobiography, and plans for future conversations. In experimental simulations, StorySage demonstrates its ability to navigate multiple sessions and capture user memories across many conversations. User studies (N=28) highlight how StorySage maintains improved conversational flow, narrative completeness, and higher user satisfaction when compared to a baseline. In summary, StorySage contributes both a novel architecture for autobiography writing and insights into how multi-agent systems can enhance human-AI creative partnerships.

  PublisherOA PDFDOI

• Local Limits of Small World Networks

  ArXiv.org · 2025-01-20

  preprintOpen accessSenior author

  Small-world networks, known for high local clustering and short path lengths, are a fundamental structure in many real-world systems, including social, biological, and technological networks. We apply the theory of (marked) local convergence (also known as Benjamini-Schramm convergence) to derive the limiting behavior of the local structures for two commonly studied small-world network models: the Watts-Strogatz and the Kleinberg models. Establishing local convergence enables us to show that key network measures, such as clustering coefficient, PageRank, greedy maximal independent set, number of spanning trees and tree entropy, converge as network size increases, with their limits determined by the graph's local structure. Additionally, this framework facilitates the estimation of global phenomena, such as the size of the giant component under bond percolation and the closely related properties, the size of the epidemic and information cascades, using local information from small neighborhoods. Furthermore, we observe a critical change in the behavior of the limit exactly when the parameter governing long-range connections in the Kleinberg model crosses the threshold where decentralized search remains efficient, offering a new perspective on why decentralized algorithms fail in certain regimes.

  PublisherOA PDFDOI

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)