Pritish Kamath

Pritish Kamath Research Scientist Google Research Contact Email:

We shape our mathematics; thereafter our mathematics shapes us…

Brief Biography

I am a research scientist at Google Research (Mountain View). My current research focus includes various topics in Differential Privacy and Machine Learning. I am also enthusiastic about Theoretical Computer Science and Mathematics, especially Computational Complexity Theory.

Lately, I have been interested in the interplay of AI and formal mathematics. I am co-teaching a course on theorem proving in Lean at UC Berkeley (with Venkat Guruswami).

I obtained my PhD. at MIT (EECS & CSAIL), where I was very fortunate to be advised by Madhu Sudan and Ronitt Rubinfeld. I used to occasionally also visit the Theory CS group at Harvard. I was a visiting student at UCLA for parts of Summers 2016 and 2017, hosted by Raghu Meka. In Summer 2018, I was a research intern at Google DeepMind (London), where I worked with Csaba Szepesvári. In Summer 2019, I was a Research Fellow in the Foundations of Deep Learning program at the Simons Insitute. Prior to joining Google, I was a post-doctoral scholar at Toyota Technological Institute, Chicago under the wise guidance of Nati Srebro.

Before coming to MIT, I graduated from IIT Bombay with a B. Tech in Computer Science and Engineering in 2012. After that, I spent a year at Microsoft Research India as a Research Assistant, where I had a great experience working under the guidance of Neeraj Kayal in the area of Algebraic Complexity.

Professional Service

CV

You can find my (perpetually outdated) CV here.

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