COVID-19 Updates

Information is available for the campus community on the Return to Learn website. Please get vaccinated and stay up to date with County and State guidelines as well as CDC recommendations.

Faculty Profile

Kamalika Chaudhuri

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.
 

Capsule Bio:

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.

Email:
kchaudhuri@ucsd.edu

Office Phone:
858-534-8909

Website





Jacobs School Faculty Update Your Profile