Akshay Krishnamurthy

Computer Science Department
School of Computer Science
Email: <my first name>kr at cs dot cmu dot edu
Office: 7507 GHC

About me:

I'm a fifth year PhD student in the Computer Science Department at Carnegie Mellon University. In May 2010, I received my undergraduate degree in EECS at UC Berkeley. I sometimes blog about my research, ultimate frisbee, and other topics that interest me.

I plan on graduating in the summer of 2015 and am looking for postdocs or other research positions.

This semester, I am co-teaching 10-704 Information Processing and Learning with Aarti Singh.


My research interests are in machine learning, from both statistical and algorithmic perspectives. I work on discovering and exploiting low dimensional structure, in the form of subspaces, clusters, or graphs in learning problems. I am specifically interested in how active learning and adaptive sampling mechanisms can be applied to these structure discovery problems. I have also worked on problems in nonparametric statistics and compressive sensing. My advisor is Aarti Singh.

Thesis Proposal: Interactive Algorithms for Unsupervised Machine Learning.


Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences, and Mutual Informations Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, and James M. Robins. [Arxiv version.]
On Estimating L_2^2 Divergence Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos and Larry Wasserman. [Arxiv version.]
On the Power of Adaptivity in Matrix Completion and Approximation. Akshay Krishnamurthy and Aarti Singh. [Arxiv version.]
Subspace Learning from Extremely Compressed Measurements. Akshay Krishnamurthy, Martin Azizyan, and Aarti Singh. [Arxiv version.]


Nonparametric Estimation of Renyi Divergence and Friends. Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, and Larry Wasserman. In International Conference on Machine Learning, ICML 2014. [ Arxiv version.][code]
Recovering Graph-Structured Activations using Adaptive Compressive Measurements. Akshay Krishnamurthy, James Sharpnack, and Aarti Singh. In Asilomar Conference on Signals, Systems and Computers, 2013. Winner of the Best Student Paper Award. [ Arxiv version.]
Low-Rank Matrix and Tensor Completion Via Adaptive Sampling Akshay Krishnamurthy and Aarti Singh. In Neural Information Processing Systems, NIPS 2013. [Arxiv version.]
Near-Optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic. James Sharpnack, Akshay Krishnamurthy, and Aarti Singh. In Neural Information Processing Systems, NIPS 2013. [Arxiv version.]
Detecting Activations over Graphs using Spanning Tree Wavelet Bases. James Sharpnack, Akshay Krishnamurthy and Aarti Singh. In Artificial Intelligence and Statistics, AISTATS 2013 (oral presentation).
Completion of high-rank ultrametric matrices using selective entries. Aarti Singh, Akshay Krishnamurthy, Sivaraman Balakrishnan and Min Xu. In International Conference on Signal Processing and Communications, SPCOM 2012.
Efficient Active Algorithms for Hierarchical Clustering. Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, and Aarti Singh. In International Conference on Machine Learning, ICML 2012. [code]

Robust Multi-Source Network Tomography using Selective Probes. Akshay Krishnamurthy and Aarti Singh. IEEE International Conference on Computer Communication, INFOCOM 2012.
Noise Thresholds for Spectral Clustering. Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh. Neural Information Processing Systems, NIPS 2011
DEGAS: De novo discovery of dysregulated pathways in human diseases. Igor Ulitsky, Akshay Krishnamurthy, Richard Karp, Ron Shamir. In PLoS ONE. October 2010.
Fine-Grained Privilege Separation for Web Applications Akshay Krishnamurthy, Adrian Mettler, and David Wagner. WWW 2010. 2010.


Spring 2015: 10-704: Information Processing and Learning (Instructor)
Spring 2013: 10-702: Statistical Machine Learning (Teaching Assistant)
Fall 2012: 15-251: Great Theoretical Ideas on Computer Science (Teaching Assistant)


Please excuse (or notify me) of any typos
Some Notes on Nonparametric Goodness-of-Fit Testing