World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
74
Citations
23709
World Ranking
1484
National Ranking
774

Research.com Recognitions

  • 2015 - ACM Fellow For contributions to algorithms for convex sets and probability distributions.
  • 2005 - Fellow of John Simon Guggenheim Memorial Foundation
  • 2002 - Fellow of Alfred P. Sloan Foundation

Overview

Santosh Vempala is affiliated with the Georgia Institute of Technology in the United States. Their research spans primarily the fields of Computer Science and Mathematics, with a notable focus on Artificial Intelligence and Computational Theory and Mathematics. Their work also covers areas including Statistics and Probability, Statistical and Nonlinear Physics, and Computational Mechanics.

The scientist has contributed extensively to a variety of topics within their fields, including:

  • Stochastic Gradient Optimization Techniques
  • Machine Learning and Algorithms
  • Markov Chains and Monte Carlo Methods
  • Complexity and Algorithms in Graphs
  • Sparse and Compressive Sensing Techniques
  • Matrix Theory and Algorithms
  • Statistical Methods and Inference

Santosh Vempala's frequent co-authors include Yin Tat Lee, Richard Peng, Yunbum Kook, Aditi Laddha, and Max Dabagia, reflecting collaboration across multiple projects and disciplines.

The scientist has published extensively in various venues, including:

  • arXiv (Cornell University)
  • Proceedings of the National Academy of Sciences
  • Random Structures and Algorithms
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Discrete & Computational Geometry

Selected recent papers authored or co-authored by Santosh Vempala include:

  • "Brain computation by assemblies of neurons," 2020, Proceedings of the National Academy of Sciences
  • "Convergence of Gibbs Sampling: Coordinate Hit-and-Run Mixes Fast," 2023, Discrete & Computational Geometry
  • "The complexity of human computation via a concrete model with an application to passwords," 2020, Proceedings of the National Academy of Sciences
  • "Mechanistic model-driven exometabolomic characterisation of human dopaminergic neuronal metabolism," 2021, bioRxiv (Cold Spring Harbor Laboratory)
  • "Approximating sparse graphs: The random overlapping communities model," 2022, Random Structures and Algorithms

Awards recognizing their contributions include the ACM Fellow in 2015, awarded for work on algorithms for convex sets and probability distributions, as well as fellowships from the John Simon Guggenheim Memorial Foundation in 2005 and the Alfred P. Sloan Foundation in 2002.

Best Publications

  • Latent semantic indexing: a probabilistic analysis

    Christos H. Papadimitriou;Hisao Tamaki;Prabhakar Raghavan;Santosh Vempala

  • On clusterings: Good, bad and spectral

    Ravi Kannan;Santosh Vempala;Adrian Vetta

  • Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

    Laurent Heirendt;Sylvain Arreckx;Thomas Pfau;Sebastián N. Mendoza

  • Fast monte-carlo algorithms for finding low-rank approximations

    Alan Frieze;Ravi Kannan;Santosh Vempala

  • Efficient algorithms for online decision problems

    Adam Kalai;Santosh Vempala

  • Matrix Approximation and Projective Clustering via Volume Sampling

    Amit Deshpande;Luis Rademacher;Santosh S. Vempala;Grant Wang

  • Clustering Large Graphs via the Singular Value Decomposition

    P. Drineas;A. Frieze;R. Kannan;S. Vempala

  • The random projection method

    Santosh S. Vempala

  • Latent Semantic Indexing

    Christos H. Papadimitriou;Prabhakar Raghavan;Hisao Tamaki;Santosh Vempala

  • On clusterings-good, bad and spectral

    R. Kannan;S. Vempala;A. Veta

  • Simulated annealing in convex bodies and an O*(n/sup 4/) volume algorithm

    L. Lovasz;S. Vempala

  • Filtering spam with behavioral blacklisting

    Anirudh Ramachandran;Nick Feamster;Santosh Vempala

  • An algorithmic theory of learning: robust concepts and random projection

    R.I. Arriaga;S. Vempala

  • The geometry of logconcave functions and sampling algorithms

    László Lovász;Santosh Vempala

  • The Spectral Method for General Mixture Models

    Ravindran Kannan;Hadi Salmasian;Santosh Vempala

  • A spectral algorithm for learning mixture models

    Santosh Vempala;Grant Wang

  • New Approximation Guarantees for Minimum-Weight k -Trees and Prize-Collecting Salesmen

    Baruch Awerbuch;Yossi Azar;Avrim Blum;Santosh Vempala

  • Path splicing

    Murtaza Motiwala;Megan Elmore;Nick Feamster;Santosh Vempala

  • Solving convex programs by random walks

    Dimitris Bertsimas;Santosh Vempala

  • Hit-and-Run from a Corner

    László Lovász;Santosh Vempala

  • Agnostic Estimation of Mean and Covariance

    Kevin A. Lai;Anup B. Rao;Santosh Vempala

Frequent Co-Authors

Ravi Kannan
Ravi Kannan Microsoft (United States)
Yin Tat Lee
Yin Tat Lee Microsoft (United States)
Christos H. Papadimitriou
Christos H. Papadimitriou Columbia University
Avrim Blum
Avrim Blum Toyota Technological Institute at Chicago
Vitaly Feldman
Vitaly Feldman Apple (United States)
Wolfgang Maass
Wolfgang Maass Graz University of Technology
Alan Frieze
Alan Frieze Carnegie Mellon University
Adam Tauman Kalai
Adam Tauman Kalai Microsoft (United States)
Maria-Florina Balcan
Maria-Florina Balcan Carnegie Mellon University
Prasad Tetali
Prasad Tetali Carnegie Mellon University

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