I am a postdoctoral researcher at UC Santa Cruz, working with Prof. C. Seshadhri. I obtained my PhD in Computer Science from Dartmouth College, where I was advised by Prof. Amit Chakrabarti. I completed my masters at the Indian Institute of Technology Delhi (IIT Delhi) under the supervision of Prof. Amit Kumar. Before that, I was an undergraduate student at Jadavpur University. Somewhere in between, I have spent a couple of years at IBM Research Lab (New Delhi) and Adobe India.

My research is broadly on the topic of foundations of data science. In particular, I am interested in large graph analysis. My work lies in the intersection of theoretical computer science and data mining. I am also interested in algorithmic fairness. In the past, I have enjoyed working on approximation algorithms and arithmetic circuit complexity.

How to count triangles, without seeing the whole graph
*Knowledge Discovery and Data Mining* *(KDD 2020)*.

Graph Coloring via Degeneracy in Streaming and Other Space-Conscious Models
*International Colloquium on Automata, Languages and Programming* *(ICALP 2020)*.

Linear Time Subgraph Counting, Graph Degeneracy, and the Chasm at Size Six
*Innovations in Theoretical Computer Science* (*ITCS 2020*).

Fair algorithms for clustering
The Conference on Neural Information Processing Systems (
*NeurIPS 2019)*.

Towards tighter space bounds for counting triangles and other substructures in graph streams
*Symposium on Theoretical Aspects of Computer Science* (*STACS 2017*).

A depth-five lower bound for iterated matrix multiplication
*Conference on Computational Complexity* (*CCC 2015*).

Minimizing average flow-time under knapsack constraint
*Theoretical Computer Science, 2016.* Extended abstract in COCOON, 2014.

Approximation algorithms for the partition vertex cover problem
*Theoretical Computer Science, 2014.*. Extended abstract in Workshop on Algorithms and Computation (WALCOM 2013).

Streaming quotient filter: A near optimal approximate duplicate detection approach for data streams
*International Conference on Very Large Data Bases* (*VLDB 2013*).