Featured Projects

Discrete Morse Theory for Persistence

Discrete Morse theory is a wonderful tool from combinatorial algebraic topology which transports Morse theory from smooth manifolds to the realm of cell complexes. Critical cells of discrete Morse functions on a given complex generate a new (and smaller) complex possessing the same homology groups. We apply discrete Morse theory to filtrations of complexes and provide a novel approach to efficient computation of persistent homology.

Here is a link to the associated software project which computes persistent homology very fast.

This is joint work with Konstantin Mischaikow.

Reconstructing Functions from Dense Samples

Given a compact Riemannian submanifold of Euclidean space, it is possible to recover (with high confidence) the homotopy type of this manifold from a sufficiently dense uniform point sample. In our work, we demonstrate a similar probabilistic result for functions. We show that under mild assumptions it is possible to reconstruct a continuous function between two such manifolds up to homology if we are given: a) dense point samples taken from the domain and the range, and b) the images of the domain samples under the action of the function. The result is robust under perturbations arising from bounded sampling noise.

This project is joint work with Konstantin Mischaikow.

A Topological Classification of Protein Compressibility

Joint work with Marcio Gameiro, Yasuaki Hiraoka, Miroslav Kramar and Konstantin Mischaikow. Details coming soon!

Information

I have been a doctoral student of Mathematics at Rutgers University in New Jersey since August 2006. My advisor is Konstantin Mischaikow. Before coming to Rutgers, I earned a Bachelor's degree in Computer Engineering as well as a Master's degree in Mathematics at Georgia Tech in Atlanta, Georgia.

Contact

511 Hill Center
110 Frelinghusen Road
Piscataway, NJ 08854
USA
Map
Email: vidit at math

Research Focus

My area of specialization is the development of topological tools for analyzing large and potentially high-dimensional datasets which arise from measurements made in a variety of contexts. Sources of data include MRI images from brain scientists, finite element annealing simulations from material scientists, and genetic information from molecular biologists among many others.