Assistant Professor, University of Texas, Austin
Eric Price studies how algorithms can produce more accurate results with less data. Two themes of his research are adaptivity–where initial data can guide future data collection, and signal structure–where a structural assumption, such as from sparsity or deep learning, can yield provable improvements in sample complexity. He recently received the NSF CAREER Award, his research was featured in Technology Review’s TR10 list of 10 breakthrough technologies of 2012, and his dissertation won the George M. Sprowls Award for best computer science thesis at MIT.
Foundations of Data Science , Fall 2018Visiting Scientist
Real-Time Decision Making , Spring 2018Visiting Scientist
Theoretical Foundations of Big Data Analysis , Fall 2013Research Fellow