Our vision is data-driven machine learning systems that advance the quality of healthcare, the understanding of cyber arms races and the delivery of online education.
Our goal is to invent applied system technologies that exploit state of art AI and machine 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.
Our goal is to help medical researchers and clincians understand the growing repositories of waveform and signal data collected from critically ill patients.
Our goal is to understand the nature of cyber security arms races between malicious and bonafide parties. Our vision is autonomous cyber defenses that anticipate and take measures against counter attacks.
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.
By crunching 130 million mouse-clicks, two CSAIL researchers have developed a machine-learning model that can predict with surprising accuracy whether or not a MOOC student will drop out of a given course.
Members of the CSAIL AnyScale Learning For All (ALFA) were honored with the best paper award at the 2012 EvoPAR track of the Evo-Applications conference. The paper, "FlexGP: Genetic programming on the cloud," is co-authored by Dylan Sherry, Kalyan Veeramachaneni, James McDermott and Una-May O'Reilly.
The paper presents a prototyped design for a decentralized, robust genetic programming algorithm capable of operating on the cloud.
ALFA is lead by Dr. Una-May O'Reilly, a principal research scientist at CSAIL. The group is focused on developing efficient ways to solve hard optimization and design problems in domains of high complexity.
