Machine Learning in in vivo CNS Drug Discovery
Jeff Schneider, Robotics Institute, CMU


Researchers in machine learning and operations research have made great strides in modeling and optimization manufacturing and other commercial processes. A more recent trend is to observe that the scientific method is a process that can be modeled and optimized with similar techniques. In this talk we consider a specific example of that: discovery of central nervous system drugs (e.g. antidepressants antipsychotics, anxiolytics, etc.) using in vivo behavioral testing. Algorithms will be discussed in the following areas: the use of kernel density estimators to provide improved posterior probabilities in multi-class applications; the use of semi-supervised learning to handle training data with uncertain class labels; and the use of active learning to control experimentation in the discovery process.


Jeff Schneider is an associate research professor in the Robotics Institute at Carnegie Mellon University. He received his PhD in Computer Science from the University of Rochester. His research interests span a wide range of machine learning areas and their use in commercial, scientific, and government applications.

Venue, Date, and Time

Venue: NSH 1507

Date: Monday October 22

Time: 12:00 noon