Sidhanth Mohanty

Email: sidhanthm (at) cs (dot) berkeley (dot) edu

Office: 634 Soda Hall

I am a PhD student in the Theory Group at UC Berkeley, fortunate to be advised by Prasad Raghavendra . Most recently, I have been thinking about problems to understand the power and limitations of semidefinite programming for average case problems. More generally, I am interested in random matrix theory, combinatorics, and geometry of polynomials. My undergraduate education was at Carnegie Mellon University where I had the fortune of being advised by Ryan O'Donnell on my senior thesis. I also had the pleasure of working with Anil Ada, Bernhard Haeupler, Ariel Procaccia, and David Woodruff.


Publications

Lifting Sum-of-Squares Lower Bounds: Degree-2 to Degree-4 [pdf]
(with Prasad Raghavendra and Jeff Xu)
Manuscript

Local Statistics, Semidefinite Programming, and Community Detection [pdf]
(with Jess Banks and Prasad Raghavendra)
Manuscript

Explicit near-Ramanujan graphs of every degree [pdf]
(with Ryan O'Donnell and Pedro Paredes)
Manuscript

The SDP value for random two-eigenvalue CSPs [pdf]
(with Ryan O'Donnell and Pedro Paredes)
Manuscript

Pseudo-deterministic Streaming
(with Shafi Goldwasser, Ofer Grossman and David P. Woodruff)
ITCS 2020

High-Dimensional Expanders from Expanders [pdf]
(with Siqi Liu and Elizabeth Yang)
ITCS 2020

X-Ramanujan Graphs [pdf]
(with Ryan O'Donnell)
SODA 2020

On Sketching the q to p norms [pdf]
(with Aditya Krishnan and David P. Woodruff)
APPROX 2018

Algorithms for Noisy Broadcast with Erasures [pdf, slides]
(with Ofer Grossman and Bernhard Haeupler)
ICALP 2018