
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
Local Statistics, Semidefinite Programming, and Community Detection
[pdf]
(with Jess Banks and Prasad Raghavendra)
Manuscript
Lifting SumofSquares Lower Bounds: Degree2 to Degree4
[pdf]
(with Prasad Raghavendra and Jeff Xu)
STOC 2020
Explicit nearRamanujan graphs of every degree
[pdf]
(with Ryan O'Donnell and Pedro Paredes)
STOC 2020
The SDP value for random twoeigenvalue CSPs
[pdf]
(with Ryan O'Donnell and Pedro Paredes)
STACS 2020
Pseudodeterministic Streaming
[pdf]
(with Shafi Goldwasser, Ofer Grossman and David P. Woodruff)
ITCS 2020
HighDimensional Expanders from Expanders
[pdf]
(with Siqi Liu and Elizabeth Yang)
ITCS 2020
XRamanujan 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