Rishikesh Gajjala
Hey there! I am a Ph.D candidate at the Indian Institute of Science (IISc). Prior to this, I did my Bachelors in Computer Science at IIT Delhi. My research is supported by PMRF. Here is my CV.
I like working on combinatorial problems. If you think you are working on a problem that I might find interesting (or want me to give you an easy-to-state but not so easy-to-solve problem), do not hesitate to reach out to me to chat about it at: rishikeshg (at) iisc (dot) ac (dot) in!
I post some of my recent academic news on twitter (@publishiperishi). (Beware of the occasional cat/dog video spam)
Research (DBLP) (In mathematics and theory CS, we usually list authors in alphabetical order)
Distributed Algorithms + Quantum Computing
No distributed quantum advantage for approximate graph coloring. [STOC 2024] [TQC 2024] (arXiv)
with Xavier Coiteux-Roy, Francesco d'Amore, Fabian Kuhn, François Le Gall, Henrik Lievonen, Augusto Modanese, Marc-Olivier Renou, Gustav Schmid, Jukka Suomela
Combinatorics + #SAT
On the smallest antichain that generates an ideal of a given size.
In review at the Journal of Combinatorial Theory, Series A. (arXiv soon!)
with Sunil Chandran and Kuldeep Meel.
Graph theory + Quantum Physics
Graph-theoretic insights on the constructability of complex entangled states.
In review at Quantum. (arXiv)
with Sunil Chandran.Edge-coloured graphs with only monochromatic perfect matchings and their connection to quantum physics.
In review at the Electronic Journal of Combinatorics. (arXiv) (video)
"Quantum-Graph Best-Paper Award" by Max Planck Institute
with Sunil Chandran.Krenn-Gu conjecture for sparse graphs.
In submission. (arXiv soon!)
with Sunil Chandran and Abraham M. Illickan
Causality
Learning Sparse Fixed-Structure Gaussian Bayesian Networks. [AISTATS 2022] (arXiv)
with Arnab Bhattacharyya, Davin Choo, Sutanu Gayen, Yuhao Wang.
Approximation algorithms + Prefix codes
Generalizations of Length Limited Huffman Coding for Hierarchical Memory Settings. [FSTTCS 2021] [DCC 2021] [DCC 2020](arXiv) (video) (slides)
with Shashwat Banchhor, Yogish Sabharwal, and Sandeep Sen.
Data compression + Deep learning
Huffman Coding Based Encoding Techniques for Fast Distributed Deep Learning. [DistributedML@CoNEXT 2020]
with Shashwat Banchhor, Ahmed M. Abdelmoniem, Aritra Dutta, Marco Canini and Panos Kalnis.