Anup Rao

About me

I am a Research Scientist at Adobe Research. Previously, I was a Postdoctoral Fellow in the School of Computer Science at Georgia Tech since August 2015. I am working with Santosh Vempala. I completed my PhD from Yale University mathematics department and Yale Institute For Network Science, my advisor was Daniel Spielman. Prior to that I received my bachelors degree in Engineering Physics from IIT Bombay.

Research (*authors in alphabetical order)

(New) Michael B. Cohen, Jonathan Kelner,Rasmus Kyng, John Peebles, Richard Peng, Anup B Rao, Aaron Sidford, Solving Directed Laplacians in Nearly Linear Time through Sparse LU Factorizations.
David Durfee, John Peebles, Richard Peng, Anup B. Rao, Determinant-Preserving Sparsification of SDDM Matrices with Applications to Counting and Sampling Spanning Trees. FOCS'17
David Durfee, Rasmus Kyng, John Peebles, Anup B. Rao, Sushant Sachdeva. Sampling Random Spanning Trees Faster than Matrix Multiplication. (STOC 2017)
Michael B. Cohen, Jonathan Kelner, John Peebles, Richard Peng, Anup B Rao, Aaron Sidford, Adrian Vladu. Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs. (STOC 2017)
Kevin Lai, Anup B. Rao, Santosh Vempala. Agnostic Estimation of Mean and Covariance. (FOCS 2016)
Rasmus Kyng, Anup B. Rao, Sushant Sachdeva. Fast, Provable Algorithms for Isotonic Regression in all l_p-norms. (NIPS 2015)
Rasmus Kyng, Anup B. Rao, Sushant Sachdeva, Daniel A. Spielman. Algorithms for Lipschitz Learning on Graphs. (COLT 2015)
Peter Chin, Anup B. Rao and Van Vu. Stochastic Block Model and Community Detection in the Sparse Graphs: A spectral algorithm with optimal rate of recovery. (COLT 2015).
Michael B. Cohen, Rasmus Kyng, Gary L. Miller, Jakub W. Pachocki, Richard Peng, Anup B. Rao and Shen Chen Xu. Solving SDD Linear Systems in Nearly mlog^1/2n Time. In STOC 2014. This paper is a merger of the following two results.

Michael B. Cohen, Rasmus Kyng, Jakub W. Pachocki, Richard Peng, and Anup B. Rao. Preconditioning in Expectation. (arXiv)
Michael B. Cohen, Gary L. Miller, Jakub W. Pachocki, Richard Peng, and Shen Chen Xu. Stretching Stretch. (arXiv)

Award

Outstanding Post-Doctoral Research: From College of Computing in Georgia Tech.

Recent and Upcoming Talks/Travels

Computational Challenges in Machine Learning, Simon's Institute, May 2017
Simons Institute of ToC, Berkeley, March-May 2017
UCSD, Feb 2017
Simons Institute of ToC, Berkeley, Jan 2017
Agnostic Estimation of Mean and Covariance, CMU, Jan 2017
Graph Sparsification with Applications, Yale, Nov 2016
Agnostic Estimation of Mean and Covariance,

FOCS, October 2016
University of Chicago, April 2016
Yale, April 2016

Community Detection in Sparse Random Graphs

Allerton Conference, October 2015
SIAM, October 2015
ARC Colloquium

Isotonic Regression

Machine Learning Seminar (Georgia Tech), January 2016

Algorithms for Lipschitz Learning

ARC Colloquium, April 2015

Representative Teaching

Spring 2016: Instructor (with Santosh Vempala), A Theoretician's Toolkit, Georgia Tech.
Summer 2014: Mentor, Geometry of Polynomials, Summer Undergraduate Math Research at Yale, Yale University
Fall 2013, Spring 2013, Fall 2011: Instructor, Calculus, Yale University

Code

Robust Mean and Covariance Estimation: Our implementation of algorithms for estimating mean and covariance in the presence of outliers from Agnostic Estimation of Mean and Covariance.
YINSlex Github Repository: Our implementations of the lex-minimization algorithms from the paper Algorithms for Lipschitz Learning on Graphs . The code was written by Rasmus Kyng, Sushant Sachdeva, Dan Spielman, and myself.
Isotonic Github Repository: An implementation of the least-squares Isotonic regression algorithm from the paper Fast, Provable Algorithms for Isotonic Regression in all ℓpℓp-norms . The code was written by Rasmus Kyng, Sushant Sachdeva, and myself.

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