2026 Thomas G. Dietterich: 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	86	746	721	393	379	315	58680

Research.com Recognitions

2007 - Fellow of the American Association for the Advancement of Science (AAAS)
2002 - ACM Fellow For contributions to machine learning.
1994 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For contributions to the science and practice of machine learning, methodology of machine learning research, and for service to the AI community.

Overview

Thomas G. Dietterich is affiliated with Oregon State University in the United States and has contributed extensively to the field of computer science, particularly in artificial intelligence. Their work spans various subfields including anomaly detection techniques, Bayesian modeling, causal inference, domain adaptation, and imbalanced data classification. Their research interests also extend into cancer research, general health professions, global and planetary change, and computer networks and communications.

The scientist's recent notable papers include:

Confidence Calibration for Domain Generalization under Covariate Shift, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Discovering Anomalies by Incorporating Feedback from an Expert, 2020, ACM Transactions on Knowledge Discovery from Data
International AI Safety Report, 2025, arXiv (Cornell University)
International Scientific Report on the Safety of Advanced AI (Interim Report), 2024, arXiv (Cornell University)
K-N-MOMDPs: Towards Interpretable Solutions for Adaptive Management, 2021, Proceedings of the AAAI Conference on Artificial Intelligence

Frequent co-authors collaborating with Thomas G. Dietterich include:

Daniel Privitera
Nicholas R. Jennings
Vidushi Marda
Helen Margetts
Arvind Narayanan

The scientist has published most frequently in venues such as arXiv (Cornell University), SuperIntelligence - Robotics - Safety & Alignment, the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), ACM Transactions on Knowledge Discovery from Data, and the Proceedings of the AAAI Conference on Artificial Intelligence.

Thomas G. Dietterich's book publication includes Learning and Reasoning, released in 2025 by Springer Science+Business Media.

Areas of study covered by their body of work can be grouped as follows:

Artificial Intelligence
Cancer Research
General Health Professions
Global and Planetary Change
Computer Networks and Communications

Research topics prominently addressed include:

Anomaly Detection Techniques and Applications
Bayesian Modeling and Causal Inference
Domain Adaptation and Few-Shot Learning
Cancer-related molecular mechanisms research
Imbalanced Data Classification Techniques
Machine Learning and Data Classification
Machine Learning and ELM

Thomas G. Dietterich's professional recognitions include:

Fellow of the American Association for the Advancement of Science (AAAS), 2007
ACM Fellow, 2002, for contributions to machine learning
Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), 1994, for contributions to machine learning science, methodology, and community service

Best Publications

Ensemble Methods in Machine Learning

Thomas G. Dietterich

10142
Citations
Approximate statistical tests for comparing supervised classification learning algorithms

Thomas G. Dietterich

4560
Citations
Solving multiclass learning problems via error-correcting output codes

Thomas G. Dietterich;Ghulum Bakiri

3898
Citations
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Thomas G. Dietterich

3775
Citations
Solving the multiple instance problem with axis-parallel rectangles

Thomas G. Dietterich;Richard H. Lathrop;Tomás Lozano-Pérez

3479
Citations
Introduction to Semi-Supervised Learning

Xiaojin Zhu;Andrew B. Goldberg;Ronald Brachman;Thomas Dietterich

2731
Citations
Machine-Learning Research

Thomas G. Dietterich

2013
Citations
Hierarchical reinforcement learning with the MAXQ value function decomposition

Thomas G. Dietterich

1987
Citations
A Unifying Review of Deep and Shallow Anomaly Detection

Lukas Ruff;Jacob R. Kauffmann;Robert A. Vandermeulen;Gregoire Montavon

1393
Citations
Adaptive computation and machine learning

Thomas Glen Dietterich

1200
Citations
The eBird enterprise: An integrated approach to development and application of citizen science

Brian L. Sullivan;Jocelyn L. Aycrigg;Jessie H. Barry;Rick E. Bonney

1164
Citations
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Dan Hendrycks;Thomas G. Dietterich

1082
Citations
Learning with many irrelevant features

Hussein Almuallim;Thomas G. Dietterich

1051
Citations
Overfitting and undercomputing in machine learning

Tom Dietterich

920
Citations
Machine Learning for Sequential Data: A Review

Thomas G. Dietterich

910
Citations
Deep Anomaly Detection with Outlier Exposure

Dan Hendrycks;Mantas Mazeika;Thomas G. Dietterich

822
Citations
Pruning Adaptive Boosting

Dragos D. Margineantu;Thomas G. Dietterich

738
Citations
Learning Boolean concepts in the presence of many irrelevant features

Hussein Almuallim;Thomas G. Dietterich

708
Citations
A reinforcement learning approach to job-shop scheduling

Wei Zhang;Thomas G. Dietterich

579
Citations
Error-correcting output coding corrects bias and variance

Eun Bae Kong;Thomas G. Dietterich

544
Citations
Multiple Classifier Systems

Gerhard Goos;Juris Hartmanis;Jan van Leeuwen

100
Citations

Frequent Co-Authors

Alan Fern Oregon State University

Prasad Tadepalli Oregon State University

Weng-Keen Wong Oregon State University

Linda G. Shapiro University of Washington

Lise Getoor University of California, Santa Cruz

Stephen Muggleton Imperial College London

Sinisa Todorovic Oregon State University

Luc De Raedt KU Leuven

Bart Selman Cornell University

Zoubin Ghahramani University of Cambridge

External Links

Wikipedia of Thomas G. Dietterich Personal website of Thomas G. Dietterich Google Scholar page

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

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens doors to numerous online learning opportunities that can help grow your skills and advance your career. Students seeking affordability often turn to the cheapest online masters programs, which provide a solid foundation in technology and research without breaking the bank. These programs are ideal for working professionals upgrading their qualifications or career-changers seeking advanced knowledge.

For those interested in leadership roles in tech-related fields, pursuing a doctorate organizational leadership can sharpen your management skills and prepare you for executive positions. Alternatively, individuals looking for a faster and more accessible route may benefit from easy online associate degrees, offering fundamental skills that can help launch your IT career quickly.

Education and technology frequently intersect. If your interests lean toward teaching, an ed d degree can help you lead educational innovation in computer science. With the flexibility and affordability of these online degrees, you can customize your educational path to suit your personal and professional goals in the dynamic field of technology.