**About me**

I am a Research Scientist at Adobe Research since August 2017. I was a Postdoctoral Fellow in the School of Computer Science at Georgia Tech with Santosh Vempala during 2015-2017 where I also worked with Richard Peng. I obtained my PhD from the mathematics department at Yale University under the guidance of Daniel Spielman where I was also affiliated with Yale Institute For Network Science. Prior to that I received my bachelors degree in engineering physics from IIT Bombay.

**Selected Publications (authors in alphabetical order unless marked *)**

- David Durfee, Yu Gao,
*Anup B. Rao*, Sebastin Wild. Efficient Second-Order Shape-Constrained Function Fitting.**WADS'19** *****Thanh Tan Nguyen, Ali Shameli, Yasin Abbasi-Yadkori,*Anup B. Rao*, Branislav Kveton. Sample Efficient Graph-Based Optimization with Noisy Queries.**AISTATS'19**- 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.**FOCS'18** - 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^{1/2}n Time. In. This paper is a merger of the following two results.**STOC 2014**- 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)

- Michael B. Cohen, Rasmus Kyng, Jakub W. Pachocki, Richard Peng, and

**Award**

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

**Talks/Travels**

- ICML, 2018
- 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:****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.