Tanvi N. Jadhav

About Me

I am a Research and Development Engineer at UC San Diego where I am a part of the WCSNG group, advised by Prof. Dinesh Bharadia. I am currently working on developing a Self-Supervised Machine Learning model which together with radar sensing can make autonomous driving better.

My research interest is in improving the mathematical understanding of modern machine learning - including problems like generalization in DNNs, training dynamics, the very good performance of optimization algorithms in non-convex settings and the kind of features learnt by these algorithms. I want to employ both theoretical and empirical methods to this end.

Previously, I was a Systems Engineer at Kumu Networks where I worked on the design, implementation and testing of Self Intereference Cancellation (SLIC) algorithms to enable Full Duplex Communication. Before that, I completed my M.S. in Electrical and Computer Engineering with communications systems as my major in May 2019 at the University of Michigan, Ann Arbor. While at Michigan, my research focus was on Network Information Theory, where I was characterizing the finite block length of the codes used in a communication-co-operation setting. I was fortunate to be advised by Prof. S. Sandeep Pradhan.

And before that, I completed my B.E.(Hons.) in Electrical and Electronics Engineering from BITS Pilani, K. K. Birla Goa Campus, India in 2017, where I worked on a wide variety of research projects including ones on optical communications, Wireless Sensor Networks and Visible Light Communication

Please find my CV here and my Google Scholar page here.

News

- Attended the UT Austin Machine Learning Lab Symposium, April 2024.

- Attended the 2023 IEEE North American School on Information Theory, June 2023.

- Attended the Deep Learning Theory Summer School and Workshop, Simons Institute for the Theory of Computing, August 2022.

- Attended the International Conference on Machine Learning (ICML), July 2022.

- Attended the Conference on Learning Theory (COLT), August 2021.

- Attended the IEEE International Symposium on Information Theory (ISIT), July 2021.

- Attended the 2021 IEEE North American School on Information Theory, June 2021.