Formulating Big Data Science Innovations for All
November 6, 2014
Speaker: Una-May O'Reilly (MIT)
Title: Formulating Big Data Science Innovations for All
Date: Thursday November 6, 2014
Location: Slonim Conference room (#430)
Goldberg Computer Science Building
6050 University Avenue, Halifax
In the spirit of B2B and B2C transactions the ALFA* group at MIT CSAIL ** is formulating I2A: Innovators to All. A (big) data science group, with foundations in scalable machine learning and evolutionary algorithms, we work on extracting insights from data in domains of social relevance. I will describe the range of our machine learning platforms including Delphi, a multi-parameter, multi-algorithm SaaS and one of our projects related to medical research (BeatDB). Time permitting, I will describe our mission to seed community-based MOOC analytics by developing a global data standard and MOOC-customizations that engage the crowd for its help.
*ALFA stands for Anyscale Learning For All
** CSAIL stands for Computer Science and Artificial Intelligence Laboratoy
Una-May O'Reilly leads the AnyScale Learning For All (ALFA) Group at MIT CSAIL. ALFA educates the forthcoming generation of data scientists, teaching them how to address the challenges spanning data integration to knowledge extraction. She has expertise in scalable machine learning, evolutionary algorithms, and frameworks for large scale knowledge mining, prediction and analytics. ALFA 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.
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 with two other patents under filing. She holds a B.Sc. from the University of Calgary, and a M.C.S. and Ph.D. (1995) from Carleton University, Ottawa, Canada.