2026 Una-May O'Reilly: 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	42	8359	8123	3584	3449	283	7694

Una-May O'Reilly 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 Una-May O'Reilly sits on this spectrum.

32 publications 991+

This scientist: 283 publications — 70th percentile

70% of scientists in this discipline score the same or lower.

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

Una-May O'Reilly 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 Una-May O'Reilly sits on this spectrum.

30 D-Index 131+

This scientist: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

Una-May O'Reilly is affiliated with MIT in the United States and is an active researcher in the field of computer science. Their work spans multiple subfields including artificial intelligence, information systems, computer vision and pattern recognition, signal processing, and computer networks and communications.

Their research focuses on several main topics such as evolutionary algorithms and applications, generative adversarial networks and image synthesis, reinforcement learning in robotics, advanced malware detection techniques, network security and intrusion detection, software engineering research, and metaheuristic optimization algorithms research.

Una-May O'Reilly has contributed extensively to research literature, with notable recent publications including:

Comprehension of computer code relies primarily on domain-general executive brain regions, 2020, eLife
Spatial Coevolution for Generative Adversarial Network Training, 2021, ACM Transactions on Evolutionary Learning and Optimization
Evolving code with a large language model, 2024, Genetic Programming and Evolvable Machines
Linking Threat Tactics, Techniques, and Patterns with Defensive Weaknesses, Vulnerabilities and Affected Platform Configurations for Cyber Hunting, 2020, arXiv (Cornell University)
Evaluating efficacy of indoor non-pharmaceutical interventions against COVID-19 outbreaks with a coupled spatial-SIR agent-based simulation framework, 2022, Scientific Reports

Their collaborations include frequent co-authorship with the following researchers:

Erik Hemberg
Jamal Toutouh
Shashank Srikant
Stephen Moskal

They have published prominently in venues such as arXiv (Cornell University), the Proceedings of the Genetic and Evolutionary Computation Conference Companion, the Proceedings of the Genetic and Evolutionary Computation Conference, ACM Transactions on Evolutionary Learning and Optimization, and bioRxiv (Cold Spring Harbor Laboratory).

Una-May O'Reilly has also contributed to book publications, including a work titled Search-Based Software Engineering published by Springer Science+Business Media in 2021.

Best Publications

OpenTuner: an extensible framework for program autotuning

Jason Ansel;Shoaib Kamil;Kalyan Veeramachaneni;Jonathan Ragan-Kelley

660
Citations
Genetic and Evolutionary Computation -- GECCO-2003

Erick Cantú-Paz;James A. Foster;Kalyanmoy Deb;Lawrence David Davis

587
Citations
Meta optimization: improving compiler heuristics with machine learning

Mark Stephenson;Saman Amarasinghe;Martin Martin;Una-May O'Reilly

362
Citations
Genetic programming needs better benchmarks

James McDermott;David R. White;Sean Luke;Luca Manzoni

302
Citations
Better GP benchmarks: community survey results and proposals

David R. White;James Mcdermott;Mauro Castelli;Luca Manzoni

268
Citations
The Troubling Aspects of a Building Block Hypothesis for Genetic Programming

Una-May O'Reilly;Franz Oppacher

186
Citations
Adversarial Deep Learning for Robust Detection of Binary Encoded Malware

Abdullah Al-Dujaili;Alex Huang;Erik Hemberg;Una-May OReilly

178
Citations
Likely to stop? Predicting Stopout in Massive Open Online Courses

Colin Taylor;Kalyan Veeramachaneni;Una-May O'Reilly

163
Citations
An analysis of genetic programming

Franz Oppacher;Una-May O'Reilly

148
Citations
Evolutionary Approaches To Minimizing Network Coding Resources

Minkyu Kim;M. Medard;V. Aggarwal;U.-M. O'Reilly

137
Citations
Program Search with a Hierarchical Variable Lenght Representation: Genetic Programming, Simulated Annealing and Hill Climbing

Una-May O'Reilly;Franz Oppacher

131
Citations
Multiple regression genetic programming

Ignacio Arnaldo;Krzysztof Krawiec;Una-May O'Reilly

130
Citations
SENSING AND MANIPULATING BUILT-FOR-HUMAN ENVIRONMENTS

Rodney A. Brooks;Lijin Aryananda;Aaron Edsinger;Paul M. Fitzpatrick

129
Citations
Autotuning algorithmic choice for input sensitivity

Yufei Ding;Jason Ansel;Kalyan Veeramachaneni;Xipeng Shen

128
Citations
Using a distance metric on genetic programs to understand genetic operators

U.-M. O'Reilly

110
Citations
Comprehension of computer code relies primarily on domain-general executive brain regions.

Anna A Ivanova;Anna A Ivanova;Shashank Srikant;Yotaro Sueoka;Yotaro Sueoka;Hope H Kean;Hope H Kean

100
Citations
Building Predictive Models via Feature Synthesis

Ignacio Arnaldo;Una-May O'Reilly;Kalyan Veeramachaneni

94
Citations
Using reinforcement learning to optimize occupant comfort and energy usage in HVAC systems

Pedro Fazenda;Kalyan Veeramachaneni;Pedro Lima;Una-May O'Reilly

92
Citations
Hybridized crossover-based search techniques for program discovery

U.-M. O'Reilly;F. Oppacher

86
Citations
Distributed, multi-model, self-learning platform for machine learning

Will D. Drevo;Kalyan K. Veeramachaneni;Una-May O'Reilly

81
Citations
Proceedings of the 7th annual conference on Genetic and evolutionary computation

Hans-Georg Beyer;Una-May O'Reilly

68
Citations

Frequent Co-Authors

Muriel Medard MIT

Saman Amarasinghe MIT

Evelina Fedorenko MIT

Frank Neumann University of Adelaide

Nancy Law University of Hong Kong

Marina Umaschi Bers Tufts University

Nicola Santoro Carleton University

David E. Goldberg University of Illinois at Urbana-Champaign

William B. Langdon University College London

Sijia Liu Michigan State University

External Links

Personal website of Una-May O'Reilly 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 education in the USA opens doors to many degree options and career paths. For those planning their academic journey, it’s important to start with the right foundation—considering the college majors that align with your interests and aspirations.

If you want to upskill efficiently, you may wonder what is the easiest masters degree to pursue online. Several programs offer flexible learning and relatively straightforward coursework, making it easier to balance further study with work or personal commitments.

Affordability is another crucial factor. Students seeking an advanced qualification should research the cheapest online doctoral programs, which allow you to earn a respected degree while minimizing student debt. There are also opportunities for fast advancement through online edd programs that offer accelerated pathways to leadership roles.

By understanding these related degrees and career pathways, you can make informed choices that fit your goals, budget, and timeline in the dynamic field of computer science.