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Una-May O'Reilly

Una-May O'Reilly

D-Index & Metrics

Computer Science

D-Index
42
Citations
7694
World Ranking
8360
National Ranking
3584

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

  • Genetic and Evolutionary Computation -- GECCO-2003

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

  • Meta optimization: improving compiler heuristics with machine learning

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

  • Genetic programming needs better benchmarks

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

  • Better GP benchmarks: community survey results and proposals

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

  • The Troubling Aspects of a Building Block Hypothesis for Genetic Programming

    Una-May O'Reilly;Franz Oppacher

  • Adversarial Deep Learning for Robust Detection of Binary Encoded Malware

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

  • Likely to stop? Predicting Stopout in Massive Open Online Courses

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

  • An analysis of genetic programming

    Franz Oppacher;Una-May O'Reilly

  • Evolutionary Approaches To Minimizing Network Coding Resources

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

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

    Una-May O'Reilly;Franz Oppacher

  • Multiple regression genetic programming

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

  • SENSING AND MANIPULATING BUILT-FOR-HUMAN ENVIRONMENTS

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

  • Autotuning algorithmic choice for input sensitivity

    Yufei Ding;Jason Ansel;Kalyan Veeramachaneni;Xipeng Shen

  • Using a distance metric on genetic programs to understand genetic operators

    U.-M. O'Reilly

  • 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

  • Building Predictive Models via Feature Synthesis

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

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

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

  • Hybridized crossover-based search techniques for program discovery

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

  • Distributed, multi-model, self-learning platform for machine learning

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

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

    Hans-Georg Beyer;Una-May O'Reilly

Frequent Co-Authors

Frank Neumann
Frank Neumann University of Adelaide
Marina Umaschi Bers
Marina Umaschi Bers Tufts University
Nicola Santoro
Nicola Santoro Carleton University
David E. Goldberg
David E. Goldberg University of Illinois at Urbana-Champaign
William B. Langdon
William B. Langdon University College London
Sean Luke
Sean Luke George Mason University
Sijia Liu
Sijia Liu Michigan State University

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