Closing the Loop on Learning and Acquisition: An Interactive Approach

Speaker Name: 
Gautam Dasarathy
Speaker Title: 
Postdoctoral Fellow
Speaker Organization: 
Rice University
Start Time: 
Friday, March 9, 2018 - 11:00am
End Time: 
Friday, March 9, 2018 - 12:15pm
Location: 
E2-506
Organizer: 
Lise Getoor

Abstract: 

With rapid progress in acquiring, processing, and learning from data, the true democratization of data-driven intelligence has never seemed closer. Unfortunately, there is a catch. Machine learning algorithms have traditionally been designed independently of the systems that acquire data. As a result, there is a fundamental disconnect between their promise and their real-world applicability. An urgent need has therefore emerged for integrating the design of learning and acquisition systems. 

In this talk, I will present an approach for addressing this learning-acquisition disconnect using interactive, multi-fidelity, and compressive machine learning methods. As an example, I will consider the problem of learning graphical model structure in high-dimensional data. This will highlight how traditional (open loop) methods do not take into account data acquisition constraints that arise in applications ranging from sensor networks to calcium imaging of the brain. I will then demonstrate how one can close this loop using techniques from interactive machine learning. I will conclude the talk with some directions for future exploration that align with this exciting research agenda.

 Bio:

Gautam Dasarathy is a Postdoctoral Fellow in the Electrical and Computer Engineering department at Rice University where he works with Richard Baraniuk. Before this, he was at the Machine Learning Department at Carnegie Mellon University working with Aarti Singh. He received his Ph.D. in Electrical Engineering from the University of Wisconsin - Madison, where he was advised by Robert Nowak and Stark Draper. His research interests include topics in machine learning, signal processing, statistics, and information theory.