World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
28382
World Ranking
9924
National Ranking
4169

Overview

Matthew D. Hoffman is a researcher affiliated with Google in the United States. Their work spans the field of computer science, with a primary focus on areas such as artificial intelligence and statistics and probability. They have contributed extensively to research in subfields including computer vision and pattern recognition, as well as organic chemistry and electrical and electronic engineering.

The scientist has published 25 works within computer science. Their research topics frequently cover areas like Markov chains and Monte Carlo methods, Gaussian processes and Bayesian inference, statistical methods and inference, Bayesian methods and mixture models, asymmetric hydrogenation and catalysis, organometallic complex synthesis and catalysis, and topic modeling.

Publications by Matthew D. Hoffman have appeared in various venues. They have published 13 papers in arXiv (Cornell University), one in Nature Communications, one in Bayesian Analysis, one in bioRxiv (Cold Spring Harbor Laboratory), and one in Organometallics.

  • Underspecification Presents Challenges for Credibility in Modern Machine Learning, 2020, arXiv (Cornell University)
  • What Are Bayesian Neural Network Posteriors Really Like?, 2021, arXiv (Cornell University)
  • Lossy Compression with Gaussian Diffusion, 2022, arXiv (Cornell University)
  • tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware, 2020, arXiv (Cornell University)
  • Scalable spatiotemporal prediction with Bayesian neural fields, 2024, Nature Communications

Their frequent coauthors include Rif A. Saurous, Pavel Sountsov, Feras A. Saad, Colin Carroll, and Brian Patton. The number of collaborations is notable, with the top collaborators having contributed to multiple papers alongside them.

Best Publications

  • Stan: A Probabilistic Programming Language

    Bob Carpenter;Andrew Gelman;Matthew D. Hoffman;Daniel Lee

  • Stochastic variational inference

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

  • Online Learning for Latent Dirichlet Allocation

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

  • The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

    Matthew D. Hoffman;Andrew Gelman

  • Variational Autoencoders for Collaborative Filtering

    Dawen Liang;Rahul G. Krishnan;Matthew D. Hoffman;Tony Jebara

  • Stochastic Gradient Descent as Approximate Bayesian Inference

    Stephan Mandt;Matthew D. Hoffman;David M. Blei

  • Underspecification Presents Challenges for Credibility in Modern Machine Learning

    Alexander D'Amour;Katherine A. Heller;Dan Moldovan;Ben Adlam

  • Learning Activation Functions to Improve Deep Neural Networks

    Forest Agostinelli;Matthew D. Hoffman;Peter J. Sadowski;Pierre Baldi

  • Static and Dynamic Source Separation Using Nonnegative Factorizations: A unified view

    Paris Smaragdis;Cedric Fevotte;Gautham J. Mysore;Nasser Mohammadiha

  • Bayesian Nonparametric Matrix Factorization for Recorded Music

    David M. Blei;Perry R. Cook;Matthew Hoffman

  • Deep Probabilistic Programming

    Dustin Tran;Matthew D. Hoffman;Rif A. Saurous;Eugene Brevdo

  • TensorFlow Distributions

    Joshua V. Dillon;Ian Langmore;Dustin Tran;Eugene Brevdo

  • Sparse Stochastic Inference for Latent Dirichlet allocation

    David Mimno;Matt Hoffman;David Blei

  • Nonparametric variational inference

    Samuel Gershman;Matt Hoffman;David M. Blei

  • Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths

    Zhicheng Liu;Yang Wang;Mira Dontcheva;Matthew Hoffman

  • A variational analysis of stochastic gradient algorithms

    Stephan Mandt;Matthew D. Hoffman;David M. Blei

  • EASY AS CBA: A SIMPLE PROBABILISTIC MODEL FOR TAGGING MUSIC

    Matthew D. Hoffman;David M. Blei;Perry R. Cook

  • On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning

    Matthew D. Hoffman;Bobak Shahriari;Nando de Freitas

  • The Stan Math Library: Reverse-Mode Automatic Differentiation in C++

    Bob Carpenter;Matthew D. Hoffman;Marcus Brubaker;Daniel D. Lee

  • What Are Bayesian Neural Network Posteriors Really Like

    Pavel Izmailov;Sharad Vikram;Matthew Hoffman;Andrew Wilson

  • Generalizing Hamiltonian Monte Carlo with Neural Networks

    Daniel Levy;Matthew D. Hoffman;Jascha Sohl-Dickstein

  • Music Transformer

    Cheng-Zhi Anna Huang;Ashish Vaswani;Jakob Uszkoreit;Noam Shazeer

Frequent Co-Authors

David M. Blei
David M. Blei Columbia University
Perry R. Cook
Perry R. Cook Princeton University
Ryan P. Adams
Ryan P. Adams Princeton University
Dustin Tran
Dustin Tran Google (United States)
Hailin Jin
Hailin Jin Adobe Systems (United States)
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)
Daniel J. Lee
Daniel J. Lee Samsung (South Korea)
Aaron Hertzmann
Aaron Hertzmann Adobe Systems (United States)
Deborah Estrin
Deborah Estrin Cornell University
Caglar Gulcehre
Caglar Gulcehre DeepMind (United Kingdom)

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

If you’re interested in studying Computer Science in the USA, there are many related online degrees and career options to consider. Many students pursue degrees in environmental science, engineering, or similar technical fields. Wondering what can you do with an environmental science major? Careers range from data analysis to environmental consulting and research.

For those seeking a flexible path, consider enrolling in a computer science degree online. These programs provide accelerated learning opportunities and can help you quickly gain the skills needed for in-demand tech jobs.

Engineering is another lucrative field. Students interested in sustainability and infrastructure might explore environmental engineering schools online. Likewise, those drawn to robotics, manufacturing, or design can compare mechanical engineering degree online cost for affordable options.

Exploring these online degrees can open the door to diverse career pathways, helping you align your education with your interests and professional goals.

Best Scientists Citing Matthew D. Hoffman

Trending Scientists