Ambar Pal

E-Mail: ambar at jhu dot edu
Office: 322, Clark Hall, JHU

About Me

I am a PhD student in the Computer Science Department at the Johns Hopkins University. I am advised by René Vidal and also work with Jeremias Sulam. I am affiliated with the Mathematical Institute for Data Science and the Vision Lab at JHU. Previously I obtained my Bachelor's degree in Computer Science from IIIT Delhi. My CV can be found here.

In the past, I have been fortunate to have worked with Chetan Arora[IIT Delhi], Gaurav Sharma[NEC Labs], H.B. Acharya[RIT], Somitra Sanadhya[IIT Ropar], Rajiv Raman[IIIT Delhi] and Yan Liu[USC].

Research Interests

The central theme of my research is understanding why deep learning works so well, and what are some theoretical explanations behind its successes and failures. With this aim, I have firstly worked on understanding common training time tricks like Dropout and Batch-Normalization. Secondly, I have worked on understanding and improving adversarial attacks and defenses for neural networks, by formulating a game-theoretic framework to study them, as well as exploring principled ways to defend against adversarial examples for the data domains of Graphs and Videos.

I received the JHU MINDS Data Science Fellowship in 2020 and 2019. My research is also supported by NSF and DARPA grants.