2026 David McAllester: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	69	1916	1855	970	932	176	40241

Discipline name

D-Index

World Ranking

Current World Ranking

National Ranking

Current National Ranking

Publications

Citations

Computer Science

1916

1855

970

932

176

40241

Research.com Recognitions

1997 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For contributions to the fields of knowledge representation, reasoning, search and planning.
1957 - Fellow of John Simon Guggenheim Memorial Foundation

Overview

David McAllester is affiliated with the Toyota Technological Institute at Chicago in the United States. Their research primarily spans the fields of computer science and mathematics, with a particular focus on artificial intelligence and mathematical physics.

Their work covers a range of specialized subfields, including:

Artificial Intelligence
Mathematical Physics
Geometry and Topology
Computational Theory and Mathematics
Computer Vision and Pattern Recognition

Key research topics pursued by David McAllester encompass:

Logic, programming, and type systems
Homotopy and cohomology in algebraic topology
Advanced topology and set theory
Computability, logic, and AI algorithms
Logic, reasoning, and knowledge
Topic modeling
Natural language processing techniques

Their recent scholarly publications, mainly hosted on arXiv (Cornell University), include:

On the Mathematics of Diffusion Models (2023, arXiv)
MathZero, The Classification Problem, and Set-Theoretic Type Theory (2020, arXiv)
On-The-Fly Information Retrieval Augmentation for Language Models (2020, arXiv)
Dependent Type Theory as Related to the Bourbaki Notions of Structure and Isomorphism (2021, arXiv)

David McAllester also collaborates with other researchers, notably with Hai Wang.

The scientist has been recognized with awards such as:

Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 1997 for contributions to knowledge representation, reasoning, search, and planning
Fellow of John Simon Guggenheim Memorial Foundation in 1957

Best Publications

Object Detection with Discriminatively Trained Part-Based Models

P F Felzenszwalb;R B Girshick;D McAllester;D Ramanan

13002
Citations
Policy Gradient Methods for Reinforcement Learning with Function Approximation

Richard S Sutton;David A. McAllester;Satinder P. Singh;Yishay Mansour

6279
Citations
A discriminatively trained, multiscale, deformable part model

P. Felzenszwalb;D. McAllester;D. Ramanan

3786
Citations
Cascade object detection with deformable part models

Pedro F. Felzenszwalb;Ross B. Girshick;David McAllester

1260
Citations
Exploring Generalization in Deep Learning

Behnam Neyshabur;Srinadh Bhojanapalli;David McAllester;Nathan Srebro

989
Citations
Systematic nonlinear planning

David McAllester;David Rosenblitt

902
Citations
Resolution Theorem Proving

Unknown

552
Citations
Evidence for invariants in local search

David McAllester;Bart Selman;Henry Kautz

551
Citations
Some PAC-Bayesian theorems

David A. McAllester

521
Citations
CLP(intervals) revisited

F. Benhamou;D. McAllester;P. van Hentenryck

485
Citations
PAC-Bayesian model averaging

David A. McAllester

476
Citations
Encoding plans in propositional logic

Henry A. Kautz;David A. McAllester;Bart Selman

462
Citations
An indexed model of recursive types for foundational proof-carrying code

Andrew W. Appel;David McAllester

442
Citations
Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation

Koichiro Yamaguchi;David A. McAllester;Raquel Urtasun

415
Citations
Solving Polynomial Systems Using a Branch and Prune Approach

Pascal Van Hentenryck;David McAllester;Deepak Kapur

408
Citations
PAC Generalization Bounds for Co-training

Sanjoy Dasgupta;Michael L. Littman;David A. McAllester

406
Citations
The Communication Complexity of Correlation

P. Harsha;R. Jain;D. McAllester;J. Radhakrishnan

344
Citations
Object Detection with Grammar Models

Ross B. Girshick;Pedro F. Felzenszwalb;David A. McAllester

330
Citations
An Outlook on Truth Maintenance.

David A McAllester

300
Citations
PAC-Bayesian Stochastic Model Selection

David A. McAllester

278
Citations
Ten challenges in propositional reasoning and search

Bart Selman;Henry Kautz;David McAllester

234
Citations
Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence

David McAllester

207
Citations

Frequent Co-Authors

Kevin Gimpel Toyota Technological Institute at Chicago

Henry Kautz University of Virginia

Raquel Urtasun University of Toronto

Peter Stone The University of Texas at Austin

Bart Selman Cornell University

Robert E. Schapire Microsoft (United States)

Ross Girshick Facebook (United States)

Michael L. Littman Brown University

Yishay Mansour Tel Aviv University

Michael Collins Google (United States)

External Links

Wikipedia of David McAllester Personal website of David McAllester Google Scholar page

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

Related Online Degrees & Career Pathways

As interest in Computer Science continues to grow, many students are considering online degree options for greater flexibility and affordability. For those seeking a quicker route, there are quick masters degrees online that allow you to complete your advanced studies in less time—helpful if you want to fast-track your career.

Choosing a program that meets industry demand is crucial. Discovering the most valuable masters degrees can ensure your investment leads to rewarding job prospects in fields like data science, AI, and software engineering.

If you’re starting out or changing careers, an online associate degree in technology can be a cost- and time-effective way to build essential skills and enter the workforce sooner.

For budget-conscious students, there are also cheap online degrees fast that don’t compromise on quality. These programs make Computer Science education more accessible—without the high price tag.