Algorithms and Geometry Collaboration: Former Collaboration Scientists

Current Collaboration Scientists   |   Former Collaboration Scientists
Anand Louis

Anand Louis, Ph.D.

Anand Louis is a postdoctoral researcher in the Department of Computer Science at Princeton University. He obtained his Ph.D. in Algorithms, Combinatorics, and Optimization from the Georgia Institute of Technology in 2014; Professor Santosh Vempala was his doctoral advisor.

His research interests lie in the applications of geometry and analysis in theoretical computer science, both in designing algorithms and in proving computational lower bounds. His earlier work focused on understanding the complexity of isoperimetric problems in graphs and hypergraphs.

Igo Shinkar

Igor Shinkar, Ph.D.

Igor Shinkar is a postdoctoral researcher at the Courant Institute of Mathematical Sciences at New York University. He obtained his Ph.D. from the Weizmann Institute of Science, under the supervision of Irit Dinur.

His interests span theoretical computer science, combinatorics, probability, and the interplay between them. In particular, he is interested in local-to-global phenomena in discrete objects. These phenomena occur naturally in different contexts in theoretical computer science, such as property testing, probabilistically checkable proofs, hardness of approximation, coding theory and sublinear time algorithms.

Aravindan Vijayaraghavan

Aravindan Vijayaraghavan, Ph.D.

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.