Manuel Sabin

Email: GitHub: LinkedIn:

I have two main directions of research in Computer Science:

I received my PhD at UC Berkeley in 2020 advised by Shafi Goldwasser and Christos Papadimitriou, and I have spent past semesters visiting (reverse chronologically) Ryan Williams (MIT), Russell Impagliazzo (UCSD), Andrej Bogdanov (CUHK), and Alon Rosen (IDC Herzliya). Before all this, I spent much time in the Math Lab at CSU Sacramento receiving my BA in Mathematics and Applied Computing. See my academic CV for more and see my Research Statement for a more in-depth overview of my research directions.


Resistance AI Workshop
with the Radical AI Network
NeurIPS 2020

Manifesting the Sociotechnical: Experimenting With Methods for Social Context and Social Justice
with Ezra Goss, Lily Hu, and Stephanie Teeple
FAccT* 2020


Learning with Distributional Inverters
with Eric Binnendyk, Marco L. Carmosino, Antonina Kolokolova, and Ramyaa Ramyaa
ALT 2022

XOR Codes and Sparse Learning Parity with Noise
with Andrej Bogdanov and Prashant Nalini Vasudevan
SODA 2019

Proofs of Work from Worst-Case Assumptions
with Marshall Ball, Alon Rosen, and Prashant Nalini Vasudevan

Fine-Grained Derandomization: From Problem-Centric to Resource-Centric Complexity
with Marco L. Carmosino and Russell Impagliazzo
ICALP 2018

Average-Case Fine-Grained Hardness
with Marshall Ball, Alon Rosen, and Prashant Nalini Vasudevan
STOC 2017


On The Utility of Fine-Grained Complexity Theory

Picture of a retail clothing
      storefront with the word Theory on the wall. Picture is nighttime in Hong Kong
      with the store interior glowing through the windows and the Theory sign is backlit
      against the wall.