Our group is dedicated to developing machine learning algorithms to leverage large medical data stored in Electronic Health Records. Our approach brings to bear new methods to derive accurate, multi-dimensional models from large collections of observational data.
Our research team brings together machine learning, natural language processing experts, database architects and programmers from the Center for Computational Learning Systems (CCLS) at Columbia University along with clinicians from Columbia University Medical Center (CUMC).
The threat of climate change is one of the greatest challenges currently facing society. Given the profound impact machine learning has made on the natural sciences to which it has been applied,
In the New York City Power Grid, electricity is transmitted via primary distribution feeders between the high voltage transmission system and the household-voltage secondary system. These feeders are susceptible to different kinds of failures such as emergency isolation caused by automatic substation relays (Open Autos), failing on test, maintainence crew noticing problems and scheduled work on different sections of the feeder.
A National Science Foundation Grant (CCR-0325463):
New Directives in Predictive Learning: Rigourous Learning Machines.
Senior Team Members: Vladimir Vapnik, David Waltz, David Hessing, Eugine Ie, Alex Gammerman, Nataliya Pavlovich
Concept Learning for Vision and Planning
A National Science Foundation SGER(Small Grant for Exploratory Research): Computational Basis for Cognition: Concept Learning for Vision and Planning grant to study the Computational Basis for Cognition.
Senior Team Members: David Waltz, Yoav Freund, Eric Baum