` Una-May O'Reilly's Webpage
Portrait
Una-May O'Reilly, Ph.D.
Principal Research Scientist
email: unamay "at" csail dot mit dot edu
office: D534 Stata Center, MIT

 

Una-May O'Reilly leads the AnyScale Learning For All (ALFA) Group. She has expertise in scalable machine learning, evolutionary algorithms, and frameworks for large scale knowledge mining, prediction and analytics. ALFA educates the forthcoming generation of data scientists, teaching them how to address the challenges spanning data integration to knowledge extraction. It has contributed the building blocks founding open MOOC analytics. It also generates and mines feature repositories, using agile and scalable machine learning to pinpoint features and prediction parameters, providing demonstrations in healthcare and other domains.

Recent invited talks include:

  • MOOCS: Research Collaboration, Data Privacy and the Role of Technology, NSF Meeting on IRB, privacy and big data in Education, Nov 2014.
  • Formulating (big) Data Science Innovations for All, Institute for Big Data Analytics, Dalhousie University , Nov 2014.
  • Data Privacy and Online Education, Panel Discussion on Digital Privacy, MIT Big Data Initiative, Nov 2014.
  • The GigaBeats Project, Joint Statistical Meeting, Boston, MA, August 2014.
  • Knowledge Mining Online Learning Data, Changing How the World Learns Symposium,Taipei, Taiwan, January 2014.
  • Comparative Effectiveness using MIMIC II Clinical Data, Critical Data: Secondary Use of Data from Critical Care, January, 2014.
  • MoocDB: Taming MOOC Big Data while Fostering Collaboration in Online Education Research, MIT XTalks Series, December, 2013.
  • MoocDB: A Framework for Collaborative Online-Learning Behavioral Research, CSAIL@BigData Workshop,November, 2013.
  • Knowledge Mining Blood Pressure Waveforms: The GigaBEATS Project. Quantitative Medicine Series, October, 2013.
  • Scalable Machine Learning to Exploit Big Data for Knowledge Discovery, MIT ILP-EPOCH Taiwan Symposium, July, 2013.
  • The GigaBeats Project: Knowledge Discovery Methods for Blood Pressure Waveforms, Big Data in BioMedicine, Stanford Medical School. May 22, 2013.
  • FlexGP: A Scalable System for Factored Learning on the Cloud, Spring CSAIL Industry Affiliates Program Meeting, May 29, 2013.
  • Divide and Conquer Machine Learning to Exploit Big Data Knowledge Discovery, Information and Communication Technologies Conference, MIT. April 24, 2013.
  • Engaging Big Data and Cloud Computing for Large Scale Machine Learning and Modeling: the ECStar platform, Oxford – Stanford Conference on Big Data: Challenges and Opportunities for Human Health, 28-29 November, 2012.
  • Cloud-Scale Learning with FlexGP, GE Whitney Symposium, Oct 23-24, 2012.

The author of over 100 academic papers, in 2013 Una-May received the EvoStar Award for Outstanding Achievements in Evolutionary Computation in Europe. She is a Young/Jr Fellow of the International Society of Genetic and Evolutionary Computation, now ACM SigEVO. She is the area editor for Data Analytics and Knowledge Discovery for Genetic Programming and Evolvable Machines (Kluwer), and editor for Evolutionary Computation (MIT Press), and action editor for the Journal of Machine Learning Research. Una-May has a patent for an original genetic algorithm technique applicable to internet-based name suggestions. She holds a B.Sc. from the University of Calgary, and a M.C.S. and Ph.D. (1995) from Carleton University, Ottawa, Canada.

See the ALFA Group website for more information.

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