Aravindan Vijayaraghavan

Aravindan Vijayaraghavan is currently a postdoctoral researcher at the Courant Institute of Mathematical Sciences, NYU, and is an adjunct professor at Northwestern University. He obtained his Ph.D. from Princeton University in 2012, on ‘Beyond Worst Case Analysis in Approximation Algorithms.’ He was also a Simons Postdoctoral Research Fellow for two years with the Algorithms & Complexity Theory group at Carnegie Mellon University.

His research interests include the areas of approximation algorithms, optimization and machine learning. In his research, he often uses paradigms that go beyond traditional worst-case analysis — like average-case analysis, smoothed analysis and instance stability — to obtain better algorithmic guarantees for problems like graph partitioning, finding dense subgraphs and unsupervised learning of various probabilistic models.