John Guibas

John Guibas

Cycladic Figures

I am an engineer based in San Francisco. I am currently the co-founder and CTO of Succinct, a cryptography startup building tools for developers to build with zero-knowledge proofs.

I previously did research on database systems, machine learning, and large vision models at the Stanford DAWN Lab and NVIDIA Research. I studied computer science at Stanford before dropping out for the Thiel Fellowship.

To get in touch, email me at john@succinct.xyz or direct message me on Twitter.

Projects

SP1: a zero-knowledge virtual machine that proves the correct execution of programs compiled for the RISC-V architecture.
AFNO: a replacement for self-attention in vision transformers by mixing tokens in the Fourier domain.
ABAE: a novel stratified sampling algorithm for computing aggregations with expensive predicates.

Fun Facts

Formerly ranked Diamond (top 1% of competitive players) in League of Legends.
I am conversational in Japanese and love watching anime. Some of my favorites are Hunter x Hunter, Odd Taxi, Cyberpunk: Edgerunners, and Ousama Ranking.
Classically trained violinist (8+ years); still love listening to classical music, especially Wieniawski's Violin Concerto No. 1 in F-sharp minor, Op. 14 and Tchaikovsky's Violin Concerto in D major, Op. 35.
I occasionally angel invest in startups (i.e., Applied Compute, Ellipsis Labs, etc).
My Erdos Number is 3.

Papers

For a full list of publications and preprints, please refer to Google Scholar.

vApps: Verifiable Applications at Internet Scale[PDF]
I Zhang, K Kulkarni, T Li, D Wong, T Kim, J Guibas, U Roy, B Pellegrino, ...
arXiv preprint arXiv:2504.14809, 2025
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
TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data[PDF]
D Kang, J Guibas, PD Bailis, T Hashimoto, M Zaharia
International Conference on Management of Data (SIGMOD), 2022
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
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