Github repo
Source: XKCD 538


I am currently working on Succinct. We are building a trustless interoperability layer for Ethereum that is powered by zkSNARKs. I was previously at Stanford studying computer science. I spent most of my time there researching database systems and machine learning at the Stanford DAWN Lab and NVIDIA Research. I collaborated with Daniel Kang, Tatsu Hashimoto, Yi Sun, and Matei Zaharia on much of this work.

To get in touch, email me at or direct message me on Twitter.


[04/2023] Our paper on data analytics with ML was accepted to the ACM Journal of Data Science
[02/2023] I received the Thiel Fellowship for building Succinct
[10/2022] Talks at ZK Summit 8, Dappcon, Stanford Blockchain Conference, and Devcon on Succinct
[06/2022] My work on semantic indexes for unstructured data was presented at SIGMOD 2022
[05/2022] I will be working at 0xPARC in NYC this summer on zero knowledge proofs
[03/2022] I received the Barry M. Goldwater Scholarship for my research
[02/2022] My work on using the FFT in transformers was accepted to ICLR
[01/2022] I was a finalist for the CRA's Outstanding Researcher Award


For a full list of publications and preprints, please refer to Google Scholar. My Erdos Number is 3.

Data Management for ML-based Analytics and Beyond [PDF]
D Kang, J Guibas, P Bailis, T Hashimoto, Y Sun, M Zaharia
Journal of Data Science (JDS), 2023

Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers [PDF]
J Guibas, M Mardani, Z Li, A Tao, A Anandkumar, B Catanzaro
International Conference on Learning Representations (ICLR), 2022

Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data [PDF]
D Kang, J Guibas, P Bailis, T Hashimoto, M Zaharia
International Conference on Management of Data (SIGMOD), 2022

Accelerating Approximate Aggregation Queries with Expensive Predicates [PDF]
D Kang, J Guibas, P Bailis, T Hashimoto, Y Sun, M Zaharia
International Conference on Very Large Data Bases (VLDB), 2021

Synthetic Medical Images from Dual Generative Adversarial Networks [PDF]
J Guibas, T Virdi, P Li
Conference on Neural Information Processing Systems (NIPS ML4H), 2017