Suman Kalyan Bera

Postdoctoral Scholar

UC Santa Cruz

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.

In Submissions


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

Arxiv Version

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

Arxiv Version

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

PDF Arxiv Version

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

PDF Code Poster Slides Arxiv Version

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).



Fenchel duals for drifting adversaries