2026 Kevin Leyton-Brown: 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	59	3337	3241	132	124	194	23427

Kevin Leyton-Brown publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Kevin Leyton-Brown sits on this spectrum.

32 publications 991+

This scientist: 194 publications — 44th percentile

44% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Kevin Leyton-Brown D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Kevin Leyton-Brown sits on this spectrum.

30 D-Index 131+

This scientist: 59 D-Index — 77th percentile

77% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

2020 - ACM Fellow For contributions to artificial intelligence, including computational game theory, multi-agent systems, machine learning, and optimization
2018 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to machine learning for algorithm optimization, and theoretical and practical aspects of computational game theory and market design.
2018 - ACM Distinguished Member

Overview

Kevin Leyton-Brown is affiliated with the University of British Columbia in Canada. Their research contributions focus primarily on the field of Computer Science, with a significant concentration in Artificial Intelligence and related subfields.

The main areas of study covered in their work include:

Artificial Intelligence
Management Science and Operations Research
Computer Networks and Communications
Information Systems
Transportation

Leyton-Brown's research topics involve a mixture of theoretical and applied aspects, notably:

Topic Modeling
Natural Language Processing Techniques
Auction Theory and Applications
Machine Learning and Data Classification
Machine Learning and Algorithms
Optimization and Search Problems
Software Engineering Research

The scholar has published extensively, with recent publications including:

In-Context Retrieval-Augmented Language Models (2023), published in Transactions of the Association for Computational Linguistics
Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence (2022), published in arXiv (Cornell University)
The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models (2022), presented at the 2022 ACM Conference on Fairness, Accountability, and Transparency
MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning (2022), published in arXiv (Cornell University)
Predicting Propositional Satisfiability via End-to-End Learning (2020), published in Proceedings of the AAAI Conference on Artificial Intelligence

Frequent co-authors collaborating with Leyton-Brown include:

Yoav Levine
Yoav Shoham
Greg d'Eon
Amnon Shashua
Hedayat Zarkoob

The preferred venues for Leyton-Brown's publications highlight their active role in the AI research community, and include:

arXiv (Cornell University)
Proceedings of the AAAI Conference on Artificial Intelligence
Transactions of the Association for Computational Linguistics
2022 ACM Conference on Fairness, Accountability, and Transparency
Artificial Intelligence

Awards recognizing contributions to the field are:

ACM Fellow (2020) for contributions to artificial intelligence, including computational game theory, multi-agent systems, machine learning, and optimization
ACM Distinguished Member (2018)
Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) (2018) for significant contributions to machine learning for algorithm optimization, and theoretical and practical aspects of computational game theory and market design

Best Publications

Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations

Yoav Shoham;Kevin Leyton-Brown

3239
Citations
Sequential model-based optimization for general algorithm configuration

Frank Hutter;Holger H. Hoos;Kevin Leyton-Brown

2834
Citations
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms

Chris Thornton;Frank Hutter;Holger H. Hoos;Kevin Leyton-Brown

1770
Citations
SATzilla: portfolio-based algorithm selection for SAT

Lin Xu;Frank Hutter;Holger H. Hoos;Kevin Leyton-Brown

1056
Citations
ParamILS: An Automatic Algorithm Configuration Framework

Frank Hutter;Thomas Stuetzle;Kevin Leyton-Brown;Holger H. Hoos

969
Citations
Incentives for sharing in peer-to-peer networks

Philippe Golle;Kevin Leyton-Brown;Ilya Mironov

862
Citations
Incentives for Sharing in Peer-to-Peer Networks

Philippe Golle;Kevin Leyton-Brown;Ilya Mironov;Mark Lillibridge

853
Citations
Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA

Lars Kotthoff;Chris Thornton;Holger H. Hoos;Frank Hutter

796
Citations
Essentials of Game Theory: A Concise, Multidisciplinary Introduction

Kevin Leyton-Brown;Yoav Shoham

703
Citations
Taming the Computational Complexity of Combinatorial Auctions: Optimal and Approximate Approaches

Yuzo Fujishima;Kevin Leyton-Brown;Yoav Shoham

687
Citations
Algorithm runtime prediction: methods & evaluation

Frank Hutter;Lin Xu;Holger H. Hoos;Kevin Leyton-Brown

552
Citations
Towards a universal test suite for combinatorial auction algorithms

Kevin Leyton-Brown;Mark Pearson;Yoav Shoham

515
Citations
Performance prediction and automated tuning of randomized and parametric algorithms

Frank Hutter;Youssef Hamadi;Holger H. Hoos;Kevin Leyton-Brown

483
Citations
Artificial intelligence and life in 2030: the one hundred year study on artificial intelligence

Peter Stone;Rodney Brooks;Erik Brynjolfsson;Ryan Calo

394
Citations
An Efficient Approach for Assessing Hyperparameter Importance

Frank Hutter;Holger Hoos;Kevin Leyton-Brown

388
Citations
Understanding random SAT: beyond the clauses-to-variables ratio

Eugene Nudelman;Kevin Leyton-Brown;Holger H. Hoos;Alex Devkar

254
Citations
ASlib: A Benchmark Library for Algorithm Selection

Bernd Bischl;Pascal Kerschke;Lars Kotthoff;Marius Thomas Lindauer

252
Citations
Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions

Kevin Leyton-Brown;Eugene Nudelman;Yoav Shoham

245
Citations
SATenstein: Automatically building local search SAT solvers from components

Ashiqur R. KhudaBukhsh;Lin Xu;Holger H. Hoos;Kevin Leyton-Brown

241
Citations
Run the GAMUT: A Comprehensive Approach to Evaluating Game-Theoretic Algorithms

Eugene Nudelman;Jennifer Wortman;Yoav Shoham;Kevin Leyton-Brown

233
Citations
Deep IV: a flexible approach for counterfactual prediction

Jason Hartford;Greg Lewis;Kevin Leyton-Brown;Matt Taddy

178
Citations
Essentials of game theory

Kevin Leyton-Brown;Yoav Shoham

55
Citations

Frequent Co-Authors

Holger H. Hoos RWTH Aachen University

Frank Hutter University of Freiburg

Yoav Shoham Stanford University

Moshe Tennenholtz Technion – Israel Institute of Technology

Milind Tambe Harvard University

Anne Condon University of British Columbia

Nicole Immorlica Microsoft (United States)

Tuomas Sandholm Carnegie Mellon University

Christopher Kiekintveld The University of Texas at El Paso

Robert Kleinberg Cornell University

External Links

Wikipedia of Kevin Leyton-Brown Personal website of Kevin Leyton-Brown Google Scholar page

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