Ashwin Pananjady

 

PhD Student
Department of Electrical Engineering and Computer Science
University of California, Berkeley

ashwinpm (at) eecs (dot) berkeley (dot) edu
264 Cory Hall

Affiliations:
Berkeley Laboratory for Information and System Sciences (BLISS)
Berkeley Artificial Intelligence Research Lab (BAIR)

News

March 2018: Outstanding GSI award, UC Berkeley for TAing the undergraduate machine learning class EECS189
March 2018: Paper on stein kernels to appear in Annales de l’Institut Henri Poincare
February 2018: New preprint on faster rates for permutation-based models
February 2018: Paper on stability of the entropy power inequality to appear in IEEE Transactions on Information Theory
January 2018: Talk at TIFR Mumbai on Machine Learning with Permutation-based Models
December 2017: Paper on locally decodable source coding to appear in IEEE Transactions on Information Theory
November 2017: Paper on linear regression with an unknown permutation to appear in IEEE Transactions on Information Theory
November 2017: Paper on mini batch SGD to appear as oral presentation at NIPS workshop on Optimization for Machine learning (OPTML)
October 2017: Paper on ranking with partial comparisons to appear as oral presentation at NIPS workshop on Learning on Distributions, Functions, Graphs, and Groups
October 2017: Invited talk at Asilomar Conference on Signals and Systems on ranking with partial pairwise comparisons

About me

I am a fourth year graduate student in the EECS Department at UC Berkeley, advised by Martin Wainwright and Thomas Courtade. My thesis committee members are Martin Wainwright, Thomas Courtade, Michael Jordan, and Adityanand Guntuboyina. My interests are broadly in statistical machine learning, information theory and optimization, and I am particularly interested in the conceptual and theoretical underpinnings of models and algorithms that are useful in practice. Specific topics that I like thinking about include latent parameter estimation, ranking, shape-constrained estimation, distributed optimization, functional inequalities, missing data, and reinforcement learning. I spent the summer of 2017 at Microsoft Research Redmond working with Denny Zhou and Lihong Li.

Before coming to Berkeley, I graduated with a B.Tech in Electrical Engineering from the Indian Institute of Technology (IIT) Madras, and was fortunate to have worked with Rahul Vaze, Sounaka Mishra and Andrew Thangaraj during my bachelor’s degree.

I co-organize the BLISS seminar at Berkeley; send me an email if you would like to give a talk!