I have two main directions of research in Computer Science:
- Mathematical Theory of Algorithms: Circuit Lower Bounds, Cryptography, Fine-grained Complexity Theory, Learning Algorithms, Pseudorandomness, and how these all influence each other.
- Social Impacts of Algorithms: Interrogating the social impact of CS research, such as Algorithmic “Fairness” and Machine Learning’s use in the practice of Law, and the implicit values it often embeds.
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.
Workshops
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
Publications
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
CRYPTO 2018
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
Thesis
On The Utility of Fine-Grained Complexity Theory
2020
