I am an Assistant Professor in the Department of Statistics at Carnegie Mellon, and an affiliated faculty member of the Machine Learning Department in the School of Computer Science.

In the recent past, I was a postdoctoral researcher in the Department of Statistics, UC Berkeley, under the able guidance of Martin Wainwright and Bin Yu. Before that I was a PhD student in the Language Technologies Institute (a part of the School of Computer Science) at Carnegie Mellon University, advised by Jaime Carbonell. Through the course of my PhD, I was also extremely fortunate to have worked with John Lafferty, Alessandro Rinaldo, Aarti Singh and Larry Wasserman.

I am broadly interested in statistical machine learning. Most recently, I have been working on understanding algorithms for solving non-convex estimation problems. I am a member of the CMU Topological Statistics group.

Selected papers All papers

Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues.
Nihar Shah, Sivaraman Balakrishnan, Adityanand Guntuboyina, Martin J. Wainwright.
IEEE Transactions on Information Theory. Accepted 2016.

Statistical Guarantees for the EM Algorithm: From Population to Sample-based Analysis.
Sivaraman Balakrishnan, Martin J. Wainwright and Bin Yu.
Annals of Statistics, Accepted. 2015.

Statistical Inference For Persistent Homology: Confidence Sets For Persistence Diagrams.
Brittany Fasy, Fabrizio Lecci, Alessandro Rinaldo, Larry Wasserman, Sivaraman Balakrishnan and Aarti Singh.
Annals of Statistics, Accepted. 2014.

Noise Thresholds for Spectral Clustering.
Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy and Aarti Singh.
Neural Information Processing Systems (NIPS) 2011.

Minimax rates for homology inference.
Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sheehy, Aarti Singh, and Larry Wasserman.
Artifical Intelligence and Statistics (AISTATS) 2012.

Sparse additive functional and kernel CCA.
Sivaraman Balakrishnan, Kriti Puniyani and John Lafferty.
International Conference on Machine Learning (ICML) 2012.

Minimax Localization of Structural Information in Large Noisy Matrices.
Sivaraman Balakrishnan, Mladen Kolar, Alessandro Rinaldo, and Aarti Singh.
Neural Information Processing Systems (NIPS) 2011.

Learning Generative Models for Protein Fold Families.
Sivaraman Balakrishnan, Hetu Kamisetty, Jaime G. Carbonell, S.I. Lee and Chris Langmead.
Proteins. 2011. Vol 79, Issue 4, pp 1061-1078.

PhD Thesis

Finding and Leveraging Structure in Learning Problems.
Thesis committee: Jaime G. Carbonell, John Lafferty, Aarti Singh, Martin J. Wainwright and Larry Wasserman.

Word Cloud

Here is word cloud from paper titles. It approximately summarizes most of my current research interests.