Una-May O'Reilly

Una-May O'Reilly is an American computer scientist and leader of the AnyScale Learning For All (ALFA) programme at the MIT Computer Science and Artificial Intelligence Laboratory.

Una-May O'Reilly
O'Reilly speaks at UC Berkeley in 2012
Alma materUniversity of Calgary
Carleton University
AwardsEvoStar Award for Outstanding Contribution to Evolutionary Computation
Scientific career
InstitutionsMassachusetts Institute of Technology
ThesisAn analysis of genetic programming. (1996)

Early life and education

O'Reilly earned her undergraduate degree at the University of Calgary. She was a graduate student at the Carleton University, where she studied computer programming. During her doctorate O'Reilly worked as a graduate fellow at the Santa Fe Institute. Her dissertation was one of the first to explore genetic programming.[1] She joined the MIT Computer Science and Artificial Intelligence Laboratory as a postdoctoral fellow in 1996.[2]

Research and career

O'Reilly is a principal research scientist at the MIT Computer Science and Artificial Intelligence Laboratory, where she leads a team focussing on scalable machine learning. Her group, AnyScale Learning For All Group (ALFA), study cybersecurity,[3] rapid intelligent data analytics and the modelling of medical data.[1][4] O'Reilly has designed computational models for a variety of different problems, including calculating the financial risk of renewable energy investments and creating a flavour algorithm that replaces taste testers.[5] O'Reilly has developed statistical models to inform the design of renewable energy systems, including predicting wind speed.[6][7]

In 2013 she was awarded the EvoStar award for Outstanding Contribution to Evolutionary Computation in Europe.[8][9] O'Reilly has received various awards and honours for her work in genetic programming; including being elected to the Executive Board of the ACM Special Interest Group on Genetic and Evolutionary Computation, SIGevo (formerly International Society of Genetic and Evolutionary Computation).

Select publications
  • Kinnear, Kenneth E.; Langdon, William B.; Spector, Lee; Angeline, Peter J.; O'Reilly, Una-May (1994). Advances in Genetic Programming. MIT Press. ISBN 978-0-262-19423-5.
  • Ansel, Jason; Kamil, Shoaib; Veeramachaneni, Kalyan; Ragan-Kelley, Jonathan; Bosboom, Jeffrey; O'Reilly, Una-May; Amarasinghe, Saman (2014). "OpenTuner". Proceedings of the 23rd International Conference on Parallel Architectures and Compilation - PACT '14. New York, New York, USA: ACM Press: 303–316. doi:10.1145/2628071.2628092. ISBN 978-1-4503-2809-8.
  • Poli, Riccardo, 1961- (2008). A field guide to genetic programming. Langdon, W. B. (William B.), McPhee, Nicholas F., Koza, John R. [S.I.]: [Lulu Press], lulu.com. ISBN 978-1-4092-0073-4. OCLC 225855345.CS1 maint: multiple names: authors list (link)
  • Stephenson, Mark; Amarasinghe, Saman; Martin, Martin; O'Reilly, Una-May (2003-05-09). "Meta optimization: improving compiler heuristics with machine learning". ACM SIGPLAN Notices. 38 (5): 77–90. doi:10.1145/780822.781141. ISSN 0362-1340.
O'Reilly at the SecDef Workshop, held as part of GECCO 2019

References
  1. "Una-May O'Reilly". MIT-IBM Watson AI Lab. Retrieved 2020-09-12.
  2. "Dr Una-May O'Reilly". Crossword Cybersecurity. Retrieved 2020-09-12.
  3. "Imperial and MIT explore how our future could be shaped by AI | Imperial News | Imperial College London". Imperial News. Retrieved 2020-09-12.
  4. "STEMM CSAIL AI in Healthcare Summit". Stemm.ai. Retrieved 2020-09-12.
  5. foodnavigator-usa.com. "Givaudan to work with MIT researchers on 'flavor algorithms'". foodnavigator-usa.com. Retrieved 2020-09-12.
  6. "Siting wind farms more quickly, cheaply". MIT News | Massachusetts Institute of Technology. Retrieved 2020-09-12.
  7. "Calculating the financial risks of renewable energy". MIT News | Massachusetts Institute of Technology. Retrieved 2020-09-12.
  8. "Compatibility". app.livestorm.co. Retrieved 2020-09-12.
  9. "Evostar 2019 - Leipzig". www.evostar.org. Retrieved 2020-09-12.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.