Grants per year

## Personal profile

### Research Interests

His research interests are broadly in the field of Theoretical Computer Science, particularly, in designing efficient algorithms for problems in Combinatorial Optimization and Machine Learning. He is also interested in using paradigms that go Beyond Worst-Case Analysis to obtain good algorithmic guarantees.

### Education/Academic qualification

Computer Science, PhD, Princeton University

2009 → 2012

Computer Science, MA, Princeton University

2007 → 2009

Computer Science and Engineering, BTech, Indian Institute of Technology, Madras

2003 → 2007

## Fingerprint Dive into the research topics where Aravindan Vijayaraghavan is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

- 1 Similar Profiles

##
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.

## Grants 2016 2022

- 4 Active

## HDR TRIPODS: Collaborative Research: Institute for Data, Econometrics, Algorithms and Learning .

Hartline, J. D., Auerbach, E. J., Berry, R. A., Canay, I. A., Guo, D., Horowitz, J. L., Makarychev, K., Vijayaraghavan, A. & Wang, Z.

9/15/19 → 8/31/22

Project: Research project

## CAREER: Beyond Worst-Case Analysis: New Approaches in Approximation Algorithms and Machine Learning

3/15/17 → 2/28/22

Project: Research project

## PSC for CAREER: Beyond Worst-Case Analysis: New Approaches in Approximation Algorithms and Machine Learning

3/15/17 → 2/28/22

Project: Research project

## AitF: Collaborative Research: Algorithms for Probabilistic Inference in the Real World

9/1/16 → 8/31/20

Project: Research project

## Research Output 2010 2018

## Clustering semi-random mixtures of Gaussians

Awasthi, P. & Vijayaraghavan, A., Jan 1 2018,*35th International Conference on Machine Learning, ICML 2018.*Krause, A. & Dy, J. (eds.). International Machine Learning Society (IMLS), p. 469-494 26 p. (35th International Conference on Machine Learning, ICML 2018; vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

## Editorial: ACM-SIAM symposium on discrete algorithms (SODA) 2016 special issue

Bhattacharyya, A., Grandoni, F., Nikolov, A., Saha, B., Saurabh, S., Vijayaraghavan, A. & Zhang, Q., Jul 1 2018, In : ACM Transactions on Algorithms. 14, 3, 26.Research output: Contribution to journal › Editorial

## Optimality of approximate inference algorithms on stable instances

Lang, H., Sontag, D. & Vijayaraghavan, A., Jan 1 2018, p. 1157-1166. 10 p.Research output: Contribution to conference › Paper

## Towards learning sparsely used dictionaries with arbitrary supports

Awasthi, P. & Vijayaraghavan, A., Nov 30 2018,*Proceedings - 59th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2018.*Thorup, M. (ed.). IEEE Computer Society, p. 283-296 14 p. 8555113. (Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS; vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

## Approximation algorithms for label cover and the log-density threshold

Chlamtáč, E., Manurangsi, P., Moshkovitz, D. & Vijayaraghavan, A., Jan 1 2017,*28th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2017.*Klein, P. N. (ed.). Association for Computing Machinery, p. 900-919 20 p. (Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution