World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
7803
World Ranking
6564
National Ranking
2901

Overview

Tsachy Weissman is a researcher affiliated with Stanford University in the United States. Their work spans multiple areas within computer science and biochemistry, genetics, and molecular biology, with a strong focus on the intersection of data science and biological systems.

Their research output includes contributions to topics such as:

  • Genomics and Phylogenetic Studies
  • Algorithms and Data Compression
  • Privacy-Preserving Technologies in Data
  • Advanced Data Storage Technologies
  • Advanced Data Compression Techniques
  • DNA and Biological Computing
  • Auction Theory and Applications

Tsachy Weissman's work is often interdisciplinary, connecting areas like artificial intelligence and molecular biology. Their publications cover several subfields, including:

  • Artificial Intelligence
  • Molecular Biology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Management Science and Operations Research

They have published extensively in various academic venues. The most frequent publication venues for their work are:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • IEEE Transactions on Information Theory
  • Scientific Reports
  • IEEE Journal on Selected Areas in Information Theory

Recent papers authored or co-authored by Tsachy Weissman include:

  • The Human Pangenome Project: a global resource to map genomic diversity (2022), published in Nature
  • Optimal rates of entropy estimation over Lipschitz balls (2020), published in The Annals of Statistics
  • Neural Joint Source-Channel Coding (2024), published on arXiv (Cornell University)
  • AI-Generated Characters: Putting Deepfakes to Good Use (2022), published in CHI Conference on Human Factors in Computing Systems Extended Abstracts
  • Classification and clustering of RNA crosslink-ligation data reveal complex structures and homodimers (2022), published in Genome Research

Frequent collaborators include:

  • Berivan Isik
  • Shubham Chandak
  • Kedar Tatwawadi
  • Hanlee P. Ji
  • Yanjun Han

Tsachy Weissman's body of work comprises significant contributions to both theoretical and applied aspects of data science as they relate to biology and information theory. Their interdisciplinary approach combines advanced computational techniques with molecular biology, enabling research advancements in data compression, genomic diversity mapping, and privacy-preserving technologies.

Best Publications

  • Universal discrete denoising: known channel

    T. Weissman;E. Ordentlich;G. Seroussi;S. Verdu

  • Minimax Estimation of Functionals of Discrete Distributions

    Jiantao Jiao;Kartik Venkat;Yanjun Han;Tsachy Weissman

  • Multiterminal Source Coding Under Logarithmic Loss

    Thomas A. Courtade;Tsachy Weissman

  • Finite State Channels With Time-Invariant Deterministic Feedback

    H.H. Permuter;T. Weissman;A.J. Goldsmith

  • Capacity of the Trapdoor Channel With Feedback

    H. Permuter;P. Cuff;B. Van Roy;T. Weissman

  • Universal Estimation of Directed Information

    Jiantao Jiao;Haim H. Permuter;Lei Zhao;Young-Han Kim

  • Maximum Likelihood Estimation of Functionals of Discrete Distributions

    Jiantao Jiao;Kartik Venkat;Yanjun Han;Tsachy Weissman

  • Interpretations of Directed Information in Portfolio Theory, Data Compression, and Hypothesis Testing

    H H Permuter;Young-Han Kim;Tsachy Weissman

  • Capacity of Channels With Action-Dependent States

    T Weissman

  • On limited-delay lossy coding and filtering of individual sequences

    T. Weissman;N. Merhav

  • Source Coding With Limited-Look-Ahead Side Information at the Decoder

    T. Weissman;A. El Gamal

  • Effect of lossy compression of quality scores on variant calling

    Idoia Ochoa;Mikel Hernaez;Rachel L. Goldfeder;Tsachy Weissman

  • The Relationship Between Causal and Noncausal Mismatched Estimation in Continuous-Time AWGN Channels

    T Weissman

  • The Gaussian Channel with Noisy Feedback

    Young-Han Kim;A. Lapidoth;T. Weissman

  • The Information Lost in Erasures

    S. Verdu;T. Weissman

  • Mutual Information, Relative Entropy, and Estimation in the Poisson Channel

    R. Atar;T. Weissman

  • A discrete universal denoiser and its application to binary images

    E. Ordentlich;G. Seroussi;S. Verdu;M. Weinberger

  • Geometric Lower Bounds for Distributed Parameter Estimation Under Communication Constraints

    Yanjun Han;Ayfer Ozgur;Tsachy Weissman

  • SPRING: a next-generation compressor for FASTQ data

    Shubham Chandak;Kedar Tatwawadi;Idoia Ochoa;Mikel Hernaez

  • Optimal rates of entropy estimation over Lipschitz balls

    Yanjun Han;Jiantao Jiao;Tsachy Weissman;Yihong Wu

  • Neural Joint Source-Channel Coding

    Kristy Choi;Kedar Tatwawadi;Aditya Grover;Tsachy Weissman

  • Universal estimation of directed information

    Lei Zhao;Haim Permuter;Young-Han Kim;Tsachy Weissman

Frequent Co-Authors

Neri Merhav
Neri Merhav Technion – Israel Institute of Technology
Young-Han Kim
Young-Han Kim University of California, San Diego
Andrea Goldsmith
Andrea Goldsmith Stony Brook University
Sergio Verdu
Sergio Verdu Princeton University
Shlomo Shamai
Shlomo Shamai Technion – Israel Institute of Technology
Gadiel Seroussi
Gadiel Seroussi University of the Republic
Amos Lapidoth
Amos Lapidoth ETH Zurich
A. Salman Avestimehr
A. Salman Avestimehr University of Southern California
Sungroh Yoon
Sungroh Yoon Seoul National University
Benjamin Van Roy
Benjamin Van Roy Stanford University

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