Assistant Professor, Northwestern University

Email: sidhanth (dot) mohanty (at) northwestern (dot) edu

Office: 3510 Mudd Hall

I am a mathematician/computer scientist, generally interested in theoretical computer science and probability theory. Most recently, I have been interested in mixing times of Markov chains, high-dimensional expansion, random matrix theory, and error-correcting codes.

I am currently an Assistant Professor in the Computer Science department at Northwestern University.

My academic journey began as an undergraduate at Carnegie Mellon University, where I was lucky to be advised by Ryan O’Donnell. My PhD education was in the Theory Group at UC Berkeley, under the amazing guidance of Prasad Raghavendra. I was later a postdoc in the Theory of Computation group at MIT, where I was hosted by Sam Hopkins.

If you happen to encounter any of my work, feel free to reach out: questions and feedback are highly appreciated!

I am looking for PhD students: if you are interested in the intersection of theoretical computer science and probability theory, do consider applying to Northwestern. Check out my note on why I think Northwestern is a fun place to be for one with these interests.

Publications

Explicit Almost-Optimal ε-Balanced Codes via Free Expander Walks [pdf]
Jun-Ting Hsieh, Sidhanth Mohanty, Rachel Yun Zhang
  Manuscript

Rigorous Implications of the Low-Degree Heuristic [pdf]
Jun-Ting Hsieh, Daniel M. Kane, Pravesh K. Kothari, Jerry Z. Li, Sidhanth Mohanty, Stefan Tiegel
  STOC 2026

Eigenvalue Bounds for Random Matrices via Zerofreeness [pdf]
Sidhanth Mohanty, Amit Rajaraman
  Manuscript

Sparsifying Cayley Graphs on Every Group [pdf]
Jun-Ting Hsieh, Daniel Z. Lee, Sidhanth Mohanty, Aaron Putterman, Rachel Yun Zhang
  SODA 2026

Explicit Lossless Vertex Expanders [pdf]
Jun‑Ting Hsieh, Alexander Lubotzky, Sidhanth Mohanty, Assaf Reiner, Rachel Yun Zhang
  FOCS 2025
  Best Paper Award

Explicit Two‑Sided Vertex Expanders Beyond the Spectral Barrier [pdf]
Jun‑Ting Hsieh, Ting‑Chun Lin, Sidhanth Mohanty, Ryan O’Donnell, Rachel Yun Zhang
  STOC 2025
  Invited to SICOMP Special Issue for STOC 2025

Weak Poincaré Inequalities, Simulated Annealing, and Sampling from Spherical Spin Glasses [pdf]
Brice Huang, Sidhanth Mohanty, Amit Rajaraman, David X. Wu
  STOC 2025

Locally Stationary Distributions: A Framework for Analyzing Slow‑Mixing Markov Chains [pdf]
Kuikui Liu, Sidhanth Mohanty, Prasad Raghavendra, Amit Rajaraman, David X. Wu
  FOCS 2024

Fast Mixing in Sparse Random Ising Models [pdf]
Kuikui Liu, Sidhanth Mohanty, Amit Rajaraman, David X. Wu
  FOCS 2024

Robust recovery for stochastic block models, simplified and generalized [pdf]
Sidhanth Mohanty, Prasad Raghavendra, David X. Wu
  STOC 2024

Explicit two‑sided unique‑neighbor expanders [pdf]
Jun‑Ting Hsieh, Theo McKenzie, Sidhanth Mohanty, Pedro Paredes
  STOC 2024

Small Even Covers, Locally Decodable Codes and Restricted Subgraphs of Edge‑Colored Kikuchi Graphs [pdf]
Jun‑Ting Hsieh, Pravesh K. Kothari, Sidhanth Mohanty, David Munhá Correia, Benny Sudakov
  International Mathematics Research Notices 2025

Local and global expansion in random geometric graphs [pdf]
Siqi Liu, Sidhanth Mohanty, Tselil Schramm, Elizabeth Yang
  STOC 2023

A simple and sharper proof of the hypergraph Moore bound [pdf]
Jun‑Ting Hsieh, Pravesh K. Kothari, Sidhanth Mohanty
  SODA 2023

Testing thresholds for high‑dimensional sparse random geometric graphs [pdf]
Siqi Liu, Sidhanth Mohanty, Tselil Schramm, Elizabeth Yang
  STOC 2022
  SIAM Journal on Computing 2024
  SICOMP Special Issue for STOC 2022

Many nodal domains in random regular graphs [pdf]
Shirshendu Ganguly, Theo McKenzie, Sidhanth Mohanty, Nikhil Srivastava
  Communications in Mathematical Physics 2023

Certifying solution geometry in random CSPs: counts, clusters and balance [pdf]
Jun‑Ting Hsieh, Sidhanth Mohanty, Jeff Xu
  CCC 2022

On statistical inference when fixed points of belief propagation are unstable [pdf]
Siqi Liu, Sidhanth Mohanty, Prasad Raghavendra
  FOCS 2021

High‑girth near‑Ramanujan graphs with lossy vertex expansion [pdf]
Theo McKenzie, Sidhanth Mohanty
  ICALP 2021

Local Statistics, Semidefinite Programming, and Community Detection [pdf]
Jess Banks, Sidhanth Mohanty, Prasad Raghavendra
  SODA 2021

List Decodable Mean Estimation in Nearly Linear Time [pdf]
Yeshwanth Cherapanamjeri, Sidhanth Mohanty, Morris Yau
  FOCS 2020

Lifting Sum‑of‑Squares Lower Bounds: Degree‑2 to Degree‑4 [pdf]
Sidhanth Mohanty, Prasad Raghavendra, Jeff Xu
  STOC 2020

Explicit near‑Ramanujan graphs of every degree [pdf]
Sidhanth Mohanty, Ryan O’Donnell, Pedro Paredes
  STOC 2020
  SIAM Journal on Computing 2021
  SICOMP Special Issue for STOC 2020

The SDP value for random two‑eigenvalue CSPs [pdf]
Sidhanth Mohanty, Ryan O’Donnell, Pedro Paredes
  STACS 2020

Pseudo‑deterministic Streaming [pdf]
Shafi Goldwasser, Ofer Grossman, Sidhanth Mohanty, David P. Woodruff
  ITCS 2020

High‑Dimensional Expanders from Expanders [pdf]
Siqi Liu, Sidhanth Mohanty, Elizabeth Yang
  ITCS 2020

X‑Ramanujan graphs [pdf]
Sidhanth Mohanty, Ryan O’Donnell
  SODA 2020

On Sketching the q to p norms [pdf]
Aditya Krishnan, Sidhanth Mohanty, David P. Woodruff
  APPROX 2018

Algorithms for Noisy Broadcast with Erasures [pdf], [slides]
Ofer Grossman, Bernhard Haeupler, Sidhanth Mohanty
  ICALP 2018

Other exposition

Local-to-global theorems for high-dimensional expansion [pdf]

Teaching

Spring 2026
CS 496: High-Dimensional Probability in Computer Science

Winter 2026
CS 335: Introduction to Theory of Computation

Fall 2025
CS 496: The Expansion Phenomenon in Computer Science