I have two main directions of research in Computer Science:

- Mathematical Theory of Algorithms: Fine-grained Complexity Theory, Pseudorandomness, Cryptography, Circuit Lower Bounds, 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