Faculty, Computer Science and Engineering
Topics in machine-learning, in particular, clustering or unsupervised learning, online learning and privacy-preserving machine-learning.
Kamalika Chaudhuri’s research interests are in machine-learning, a subfield that lies at the intersection of statistics and computer science. She is interested in three aspects of machine-learning -- unsupervised learning, online learning and privacy-preserving machine learning. In unsupervised learning, the goal is to extract information from unlabeled data to assist various learning tasks. In online learning, data arrives one at a time, and the challenge is to make good predictions on the face of changing data and models. Privacy-preserving machine learning addresses the problem of learning a good predictor from the data, while ensuring the privacy of individuals in the training data set.
Kamalika Chaudhuri received a Bachelor of Technology degree in Computer Science and Engineering in 2002 from Indian Institute of Technology, Kanpur, and a PhD in Computer Science from University of California at Berkeley in 2007. She held a postdoctoral researcher position at the Information Theory and Applications Center at UC San Diego from 2007-2009, and a postdoctoral researcher position in the CSE department at UC San Diego from 2009-2010. In July 2010, she joined the CSE department at UC San Diego as an assistant professor.