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Rising Stars
2025

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Rising Stars

D-Index
42
Citations
6210
World Ranking
570
National Ranking
84

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Aaron Sidford is a researcher affiliated with Stanford University in the United States, specializing in computer science. Their work encompasses multiple subfields within computer science, with a strong focus on computational theory and mathematics as well as artificial intelligence. Other notable subfields in their research portfolio include computational mechanics, computer networks and communications, and statistics and probability.

Their research topics cover a range of areas, highlighting complexity and algorithms in graphs, sparse and compressive sensing techniques, stochastic gradient optimization techniques, and optimization and search problems. Additional topics include advanced graph theory research, Markov chains and Monte Carlo methods, and advanced optimization algorithms research.

Aaron Sidford has authored numerous academic papers published primarily in venues that include arXiv (Cornell University), Leibniz-Zentrum für Informatik (Schloss Dagstuhl), SIAM Journal on Computing, Operations Research Letters, and Theory of Computing. The distribution of their publications across these venues indicates active engagement with both preprint archives and specialized academic journals.

  • Towards optimal running times for optimal transport, 2023, Operations Research Letters
  • Large-Scale Methods for Distributionally Robust Optimization, 2020, arXiv (Cornell University)
  • Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary, 2022, arXiv (Cornell University)
  • Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration, 2021, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Minimum Cost Flows, MDPs, and ℓ₁-Regression in Nearly Linear Time for Dense Instances, 2021, arXiv (Cornell University)

Their frequent co-authors include Arun Jambulapati, Jan van den Brand, Yang P. Liu, Yujia Jin, and Kevin Tian. Collaboration with these researchers appears extensive, reflecting joint efforts on topics aligned with Sidford's research interests.

Best Publications

  • Path Finding Methods for Linear Programming: Solving Linear Programs in Õ(vrank) Iterations and Faster Algorithms for Maximum Flow

    Yin Tat Lee;Aaron Sidford

  • Accelerated Methods for Non-Convex Optimization

    Yair Carmon;John C. Duchi;Oliver Hinder;Aaron Sidford

  • An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations

    Jonathan A. Kelner;Yin Tat Lee;Lorenzo Orecchia;Aaron Sidford

  • A simple, combinatorial algorithm for solving SDD systems in nearly-linear time

    Jonathan A. Kelner;Lorenzo Orecchia;Aaron Sidford;Zeyuan Allen Zhu

  • Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems

    Yin Tat Lee;Aaron Sidford

  • A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization

    Yin Tat Lee;Aaron Sidford;Sam Chiu-Wai Wong

  • Uniform Sampling for Matrix Approximation

    Michael B. Cohen;Yin Tat Lee;Cameron Musco;Christopher Musco

  • Lower bounds for finding stationary points I

    Yair Carmon;John C. Duchi;Oliver Hinder;Aaron Sidford

  • Geometric median in nearly linear time

    Michael B. Cohen;Yin Tat Lee;Gary Miller;Jakub Pachocki

  • Efficient Inverse Maintenance and Faster Algorithms for Linear Programming

    Yin Tat Lee;Aaron Sidford

  • Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization

    Roy Frostig;Rong Ge;Sham Kakade;Aaron Sidford

  • Lower bounds for finding stationary points II: first-order methods

    Yair Carmon;John C. Duchi;Oliver Hinder;Aaron Sidford

  • Single Pass Spectral Sparsification In Dynamic Streams

    Michael Kapralov;Yin Tat Lee;Cameron Musco;Christopher Musco

  • Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model

    Aaron Sidford;Mengdi Wang;Xian Wu;Lin F. Yang

  • Streaming PCA: Matching matrix bernstein and near-optimal finite sample guarantees for oja's algorithm

    Prateek Jain;Chi Jin;Sham M. Kakade;Praneeth Netrapalli

  • Competing with the Empirical Risk Minimizer in a Single Pass

    Roy Frostig;Rong Ge;Sham M. Kakade;Aaron Sidford

  • Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification

    Prateek Jain;Sham M. Kakade;Rahul Kidambi;Praneeth Netrapalli

  • Variance reduced value iteration and faster algorithms for solving Markov decision processes

    Aaron Sidford;Mengdi Wang;Xian Wu;Yinyu Ye

  • Accelerating Stochastic Gradient Descent for Least Squares Regression

    Prateek Jain;Sham M. Kakade;Rahul Kidambi;Praneeth Netrapalli

  • Minimum cost flows, MDPs, and ℓ1-regression in nearly linear time for dense instances

    Jan van den Brand;Yin Tat Lee;Yang P. Liu;Thatchaphol Saranurak

Frequent Co-Authors

Yin Tat Lee
Yin Tat Lee Microsoft (United States)
John C. Duchi
John C. Duchi Stanford University
Zhao Song
Zhao Song Adobe Systems (United States)
Yinyu Ye
Yinyu Ye Stanford University
Yuanzhi Li
Yuanzhi Li Carnegie Mellon University
Shuzhong Zhang
Shuzhong Zhang University of Minnesota

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