World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
6835
World Ranking
8450
National Ranking
3612

Overview

Leonard J. Schulman is affiliated with the California Institute of Technology in the United States. Their research primarily spans the fields of Computer Science with focused contributions in Artificial Intelligence, Management Science and Operations Research, Signal Processing, and Economics and Econometrics.

The subfields of study associated with their work include:

  • Artificial Intelligence
  • Management Science and Operations Research
  • Signal Processing
  • Economics and Econometrics

Schulman's research topics cover a diverse range of areas, reflecting interdisciplinary interests. The main topics they have worked on are:

  • Machine Learning and Algorithms
  • Algorithms and Data Compression
  • Bayesian Modeling and Causal Inference
  • Blind Source Separation Techniques
  • Bayesian Methods and Mixture Models
  • Game Theory and Applications
  • Sparse and Compressive Sensing Techniques

Throughout their career, Schulman has collaborated frequently with several researchers, including:

  • Yuval Rabani
  • Spencer Gordon
  • Bijan Mazaheri
  • Krishnamurthy Dvijotham
  • Fabrizio Grandoni

They have published in various venues, with a particularly notable presence in the following journals and archives:

  • arXiv (Cornell University)
  • IEEE Transactions on Information Theory
  • Journal of Mathematical Economics
  • Games and Economic Behavior
  • Information Processing Letters

Selected recent publications include:

  • Hadamard Extensions and the Identification of Mixtures of Product Distributions, 2022, IEEE Transactions on Information Theory
  • The invisible hand of Laplace: The role of market structure in price convergence and oscillation, 2021, Journal of Mathematical Economics
  • Convergence of incentive-driven dynamics in Fisher markets, 2020, Games and Economic Behavior
  • A refined approximation for Euclidean k-means, 2022, Information Processing Letters
  • Source Identification for Mixtures of Product Distributions, 2020, arXiv (Cornell University)

Best Publications

  • The effectiveness of lloyd-type methods for the k-means problem

    Rafail Ostrovsky;Yuval Rabani;Leonard J. Schulman;Chaitanya Swamy

  • Splitters and near-optimal derandomization

    M. Naor;L.J. Schulman;A. Srinivasan

  • Coding for interactive communication

    L.J. Schulman

  • Broadcasting on trees and the Ising model

    William Evans;Claire Kenyon;Yuval Peres;Leonard J. Schulman

  • A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians

    Sanjoy Dasgupta;Leonard Schulman

  • A Two-Round Variant of EM for Gaussian Mixtures

    Sanjoy Dasgupta;Leonard J. Schulman

  • Quantum mechanical algorithms for the nonabelian hidden subgroup problem

    Michelangelo Grigni;Leonard Schulman;Monica Vazirani;Umesh Vazirani

  • Physical Limits of Heat-Bath Algorithmic Cooling

    Leonard J. Schulman;Tal Mor;Yossi Weinstein

  • Asymptotically good codes correcting insertions, deletions, and transpositions

    L.J. Schulman;D. Zuckerman

  • A random walk model of wave propagation

    M. Franceschetti;J. Bruck;L.J. Schulman

  • Communication on noisy channels: a coding theorem for computation

    L.J. Schulman

  • Deterministic coding for interactive communication

    Leonard J. Schulman

  • Universal ε-approximators for integrals

    Michael Langberg;Leonard J. Schulman

  • The Symmetric Group Defies Strong Fourier Sampling

    Cristopher Moore;Alexander Russell;Leonard J. Schulman

  • A coding theorem for distributed computation

    Sridhar Rajagopalan;Leonard Schulman

  • Molecular scale heat engines and scalable quantum computation

    Leonard J. Schulman;Umesh V. Vazirani

  • Reconstruction from subsequences

    Miroslav Dudík;Leonard J. Schulman

  • Signal propagation and noisy circuits

    W.S. Evans;L.J. Schulman

  • Lower bounds for linear locally decodable codes and private information retrieval

    O. Goldreich;H. Karloff;L.J. Schulman;L. Trevisan

  • Lower bounds for linear locally decodable codes and private information retrieval

    Oded Goldreich;Howard Karloff;Leonard J. Schulman;Luca Trevisan

  • Proceedings of the 42nd ACM symposium on Theory of computing

    Michael Mitzenmacher;Leonard J. Schulman

Frequent Co-Authors

Yuval Rabani
Yuval Rabani Hebrew University of Jerusalem
Umesh Vazirani
Umesh Vazirani University of California, Berkeley
Chaitanya Swamy
Chaitanya Swamy University of Waterloo
Cristopher Moore
Cristopher Moore Santa Fe Institute
Rafail Ostrovsky
Rafail Ostrovsky University of California, Los Angeles
Alexander Russell
Alexander Russell University of Connecticut
Michael Langberg
Michael Langberg University at Buffalo, State University of New York
Andris Ambainis
Andris Ambainis University of Latvia
Yair Bartal
Yair Bartal Hebrew University of Jerusalem
Daniel N. Rockmore
Daniel N. Rockmore Dartmouth College

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