World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
109
Citations
122521
World Ranking
230
National Ranking
129

Research.com Recognitions

  • 2017 - Fellow of John Simon Guggenheim Memorial Foundation
  • 2015 - ACM Fellow For contributions to the theory and practice of probabilistic topic modeling and Bayesian machine learning.
  • 2013 - ACM Prize in Computing For pioneering the area of topic modeling, which has had profound influence on machine learning foundations as well as industrial practice.
  • 2010 - Fellow of Alfred P. Sloan Foundation

Overview

David M. Blei is affiliated with Columbia University in the United States. Their research spans primarily the field of computer science, with a strong focus on artificial intelligence and statistics and probability. Their work also intersects with molecular biology, economics and econometrics, and immunology.

The main topics covered by their research include:

  • Statistical Methods and Inference
  • Advanced Causal Inference Techniques
  • Bayesian Modeling and Causal Inference
  • Topic Modeling
  • Gaussian Processes and Bayesian Inference
  • Bayesian Methods and Mixture Models
  • Computational and Text Analysis Methods

David M. Blei has contributed extensively to multiple venues, with frequent publications in:

  • arXiv (Cornell University)
  • The Annals of Applied Statistics
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Journal of Biomedical Informatics
  • Transactions of the Association for Computational Linguistics

Their recent papers include:

  • "Variational Inference: A Review for Statisticians" (2023), published in OPAL (Open@LaTrobe) at La Trobe University
  • "Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs" (2024), published in Nature Biotechnology
  • "Adjusting for indirectly measured confounding using large-scale propensity score" (2022), published in Journal of Biomedical Informatics
  • "SHOPPER: A probabilistic model of consumer choice with substitutes and complements" (2020), published in The Annals of Applied Statistics
  • "Counterfactual inference for consumer choice across many product categories" (2021), published in Quantitative Marketing and Economics

The scientist has frequently collaborated with several notable co-authors, including:

  • Achille Nazaret
  • Zhaoran Wang
  • John P. Cunningham
  • Claudia Shi
  • Keyon Vafa

David M. Blei's recognition includes being named an ACM Fellow in 2015 for contributions to probabilistic topic modeling and Bayesian machine learning. In 2013, they received the ACM Prize in Computing for pioneering work in topic modeling, influencing machine learning both theoretically and practically.

Additional honors include being a Fellow of the John Simon Guggenheim Memorial Foundation in 2017 and a Fellow of the Alfred P. Sloan Foundation in 2010. These awards reflect engagement with foundational aspects of machine learning, probabilistic modeling, and applied statistics.

Best Publications

  • Latent dirichlet allocation

    David M. Blei;Andrew Y. Ng;Michael I. Jordan

  • Variational Inference: A Review for Statisticians

    David M. Blei;Alp Kucukelbir;Jon D. McAuliffe

  • Probabilistic topic models

    David M. Blei

  • Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes

    Yee W. Teh;Michael I. Jordan;Matthew J. Beal;David M. Blei

  • Dynamic topic models

    David M. Blei;John D. Lafferty

  • Reading Tea Leaves: How Humans Interpret Topic Models

    Jonathan Chang;Sean Gerrish;Chong Wang;Jordan L. Boyd-graber

  • Mixed Membership Stochastic Blockmodels

    Edoardo M. Airoldi;David M. Blei;Stephen E. Fienberg;Eric P. Xing

  • Stochastic variational inference

    Matthew D. Hoffman;David M. Blei;Chong Wang;John Paisley

  • A correlated topic model of Science

    David M. Blei;John D. Lafferty

  • Matching words and pictures

    Kobus Barnard;Pinar Duygulu;David Forsyth;Nando de Freitas

  • Online Learning for Latent Dirichlet Allocation

    Matthew Hoffman;Francis R. Bach;David M. Blei

  • Collaborative topic modeling for recommending scientific articles

    Chong Wang;David M. Blei

  • Variational Inference for Dirichlet Process Mixtures

    David M. Blei;Michael I. Jordan

  • Supervised Topic Models

    David M. Blei;Jon D. Mcauliffe

  • Correlated Topic Models

    John D. Lafferty;David M. Blei

  • Modeling annotated data

    David M. Blei;Michael I. Jordan

  • Hierarchical Topic Models and the Nested Chinese Restaurant Process

    Thomas L. Griffiths;Michael I. Jordan;Joshua B. Tenenbaum;David M. Blei

  • Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding

    Paul DiMaggio;Manish Nag;David Blei

  • Black Box Variational Inference

    Rajesh Ranganath;Sean Gerrish;David M. Blei

  • The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies

    David M. Blei;Thomas L. Griffiths;Michael I. Jordan

Frequent Co-Authors

Rajesh Ranganath
Rajesh Ranganath New York University
Edoardo M. Airoldi
Edoardo M. Airoldi Temple University
Stephen E. Fienberg
Stephen E. Fienberg Carnegie Mellon University
Dustin Tran
Dustin Tran Google (United States)
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
John Paisley
John Paisley Columbia University
Matthew D. Hoffman
Matthew D. Hoffman Google (United States)
Eric P. Xing
Eric P. Xing Mohamed bin Zayed University of Artificial Intelligence
Jordan Boyd-Graber
Jordan Boyd-Graber University of Maryland, College Park
Samuel J. Gershman
Samuel J. Gershman Harvard University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education in Computer Science opens doors to a range of related programs and career opportunities. Alongside core computer science study, many students consider branching into technical areas with strong career prospects.

For those interested in expanding their expertise, the best online electrical engineering programs USA offer a blend of practical and theoretical training in areas closely related to computing. Alternatively, if you are seeking quicker credentials, check out certifications that pay well to enhance your resume and boost your employability in a short period.

Time is an important consideration for many students. The shortest master degree programs can help you gain advanced skills and credentials without spending years in school. It's also crucial to choose a graduate path with a strong return on investment; discover graduate degrees that are worth it to ensure your education leads to valuable, in-demand career options.

Best Scientists Citing David M. Blei

Trending Scientists

Recently Published Articles