Una-May O'Reilly

Principal Research Scientist





Una-May is leader of the AnyScale Learning For All (ALFA) group at CSAIL. ALFA focuses on scalable machine learning, evolutionary algorithms, and frameworks for large scale knowledge mining, prediction and analytics.  The group has projects in cybersecurity, healthcare, and online education.

Una-May was awarded the EvoStar Award for Outstanding Contribution of Evolutionary Computation in Europe in April, 2013. She is also is a Fellow of the International Society of Genetic and Evolutionary Computation, now ACM Sig-EVO. 

Una-May co-founded ACM SigEVO in 2004. She serves as Vice-Chair of ACM SigEVO. In 2013 she inaugurated the Women in Evolutionary Computation group at GECCO.

Una-May served as chair of the largest international Evolutionary Computation Conference,  GECCO, in 2005.  She has served on the GECCO business committee, co-led the 2006 and 2009 Genetic Programming: Theory to Practice Workshops and co-chaired EuroGP, the largest conference devoted to Genetic Programming.

Una-May serves as the area editor for Data Analytics and Knowledge Discovery for Genetic Programming and Evolvable Machines (Kluwer), as editor for Evolutionary Computation (MIT Press), and as action editor for the Journal of Machine Learning Research.

Una-May has a patent  for a original genetic algorithm technique applicable to internet-based name suggestions.

Una-May holds a B.Sc. from the University of Calgary, and a M.C.S. and Ph.D. (1995) from Carleton University, Ottawa, Canada. She joined the Artificial Intelligence Laboratory, MIT as a Post-Doctoral Associate in 1996.



MOOC Learner Project: Data science for e-learning

The MOOC Learner Project provides learning scientists, instructional designers and online education specialists with open source software that enables them to efficiently extract teaching and learning insights from the data collected when students learn on the edX or open edX platform.


Community of Research

Applied Machine Learning Community of Research

This CoR brings together researchers at CSAIL working across a broad swath of application domains. Within these lie novel and challenging machine learning problems serving science, social science and computer science.

Community of Research

Cognitive AI Community of Research

This CoR aims to develop AI technology that synthesizes symbolic reasoning, probabilistic reasoning for dealing with uncertainty in the world, and statistical methods for extracting and exploiting regularities in the world, into an integrated picture of intelligence that is informed by computational insights and by cognitive science.


What better wind-speed prediction can do for the energy industry

When a power company wants to build a new wind farm, it generally hires a consultant to make wind speed measurements at the proposed site for eight to 12 months. Those measurements are correlated with historical data and used to assess the site’s power-generation capacity.This month CSAIL researchers will present a new statistical technique that yields better wind-speed predictions than existing techniques do — even when it uses only three months’ worth of data. That could save power companies time and money, particularly in the evaluation of sites for offshore wind farms, where maintaining measurement stations is particularly costly.