Prior to joining the department, she received a PhD in Computer Science from UC Berkeley in 2007, and was a postdoctoral researcher at UC San Diego from 2007-2010. She is the recipient of a Hellman Faculty Fellowship and she received an NSF CAREER award in 2012. Kamalika's research is on learning theory, which deals with the theoretical foundations of machine learning. She is particularly interested in privacy-preserving machine learning -- how to learn a good predictor from sensitive training data, while ensuring the privacy of individuals in the data set.