Professional Preparation

UniversityProgramDegreeYear
Norwegian University of Science and TechnologyComputer SciencePh.D. CandidateEst. 2027
UC San DiegoArchitecture-based Enterprise Systems Engineering and Leadership ProgramM.A.S.2014
UC San DiegoInterdisciplinary Computing in the ArtsB.A.2002

Appointments

YearsTitleInstitution
2024-PresentPrinciple AI Reproducibility Staff ResearcherSan Diego Supercomputer Center
2013-2024Senior Systems and Cloud Integration EngineerSan Diego Supercomputer Center
2002-2013LMS Infrastructure & Development LeadUC San Diego

Products

  1. Coakley, Kevin L; Snelleman, Thijs; Hoos, Holger; Gundersen, Odd Erik. “The AI Reproducibility Index: Ranking Venues, Countries and Institutions by Reproducibility.” Proceedings of the 4th ACM Conference on Reproducibility and Replicability (2026). https://doi.org/10.1145/3820002.3828592
  2. Coakley, Kevin L; Snelleman, Thijs; Hoos, Holger; Gundersen, Odd Erik. “The Shift Toward Open and Reproducible AI Research.” arXiv preprint arXiv:2606.16974. https://doi.org/10.48550/arXiv.2606.16974
  3. Coakley, Kevin L and Gundersen, Odd Erik. “Measuring deep learning performance - an empirical study of performance distributions across architectures and tasks.” Scientific Reports (2026). https://doi.org/10.1038/s41598-026-49656-z
  4. Ahmed, Waqas; Samuel, Sheeba; Coakley, Kevin; Koenig-Ries, Birgitta; Gundersen, Odd Erik. “Learning to be Reproducible: Custom Loss Design for Robust Neural Networks.” Reproducible Artificial Intelligence (RAI2026). https://doi.org/10.48550/arXiv.2601.00578
  5. Kirkpatrick, Christine R.; Coakley, Kevin; Christopher, Julianne; Dutra, Inês. “Engaging with Researchers and Raising Awareness of FAIR and Open Science through the FAIR+ Implementation Survey Tool (FAIRIST).” Data Science Journal 22 (2023). https://doi.org/10.5334/dsj-2023-032
  6. Gundersen, Odd Erik and Kevin Coakley. “Open Research in Artificial Intelligence and the Search for Common Ground in Reproducibility: A Commentary on “(Why) Are Open Research Practices the Future for the Study of Language Learning?”.” Language Learning (2023). https://doi.org/10.1111/lang.12582
  7. Coakley, Kevin, Christine R. Kirkpatrick and Odd Erik Gundersen. “Examining the Effect of Implementation Factors on Deep Learning Reproducibility.” 2022 IEEE 18th International Conference on e-Science (e-Science) (2022): 397-398. https://doi.org/10.1109/eScience55777.2022.00056
  8. Gundersen, Odd Erik; Coakley, Kevin; Kirkpatrick, Christine (2022). Sources of Irreproducibility in Machine Learning: A Review. arXiv preprint arXiv:2204.07610. https://doi.org/10.48550/arXiv.2204.07610
  9. Kirkpatrick, Christine R.; Coakley, Kevin ; Cragin, Melissa H.; Cramer, Catherine; McHenry, Kenton; Marini, Luigi; Kooper, Rob; Glasgow, James; Foster, Ian T.; Szalay, Alex S.; Simmel, Derek (2021). Open Storage Network Concept Paper: Open Storage Network Retrospective & the Future of Distributed Storage for eInfrastructure. In San Diego Supercomputer Center (SDSC) Research Data Services Materials Collection. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0RR1ZCR
  10. Kirkpatrick, Christine R.; Coakley, Kevin; Cragin, Melissa H.; Glasgow, James; McHenry, Kenton; Jandt, Uwe; Krøl Andersen, Lene; Baldin, Ilya; Nikolich, Anita; Szalay, Alex S. (2021). Open Storage Network Concept Paper: National and International Trends in Research Storage at Scale. In San Diego Supercomputer Center (SDSC) Research Data Services Materials Collection. UC San Diego Library Digital Collections. https://doi.org/10.6075/J00G3HQ1
  11. Kirkpatrick, Christine R.; Coakley, Kevin; Cragin, Melissa H.; Glasgow, James; Goodhue, John (2021). Open Storage Network Concept Paper: Research Drivers and Capabilities. In San Diego Supercomputer Center (SDSC) Research Data Services Materials Collection. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0HM56Z7
  12. Kumari Sonal Choudhary, Eoin Fahy, Kevin Coakley, Manish Sud, Mano R Maurya, Shankar Subramaniam. MetENP/MetENPWeb: An R package and web application for metabolomics enrichment and pathway analysis in Metabolomics Workbench. bioRxiv, 2020.11.20.391912. https://doi.org/10.1101/2020.11.20.391912
  13. Subhasis Dasgupta, Kevin Coakley, and Amarnath Gupta. Analytics-Driven Data Ingestion and Derivation in the AWESOME Polystore. IEEE BigData, 2016. https://doi.org/10.1109/BigData.2016.7840897
  14. Sean Boucek, Kevin Coakley, Allan Oblepias, and Greg Vonder Reith. Brain Collaborative Research Database (CoRD). M.A.S. dissertation, UC San Diego, United States, 2014.

Presentations

  1. The AI Reproducibility Index: Ranking Venues, Countries and Institutions by Reproducibility; Proceedings of the 4th ACM Conference on Reproducibility and Replicability (2026)
  2. ML Reproducibility: Sources of Algorithmic, Implementation, and Observational Variability; iHARP Tuesday Talk 10-29-2024
  3. Sources of Irreproducibility in Machine Learning; FARR Workshop 2024
  4. Reproducibility in AI, and What Computing Professionals Should Know for Supporting Researchers; FARR: FAIR in ML, AI Readiness, & Reproducibility Research Coordination Network 2023
  5. Examining the Effect of Implementation Factors on Deep Learning Reproducibility; IEEE eScience 2022
  6. AI Reproducibility; Partnership on AI - Health AI Exploration: Virtual Meeting #5 2022
  7. Ethics Implications of Irreproducibility; AGU - AI/ML Ethics Workshop Series 2022
  8. Sources of Irreproducibility / Testing Pipeline using OSG and Cloud Resources; International Data Week 2022
  9. The Open Storage Network; Super Computing 2021
  10. Reproducibility in AI, and What Computing Professionals Should Know for Supporting Researchers; UC Tech 2021
  11. The Open Storage Network; PEARC 2021