John Guibas

Contact

• @jtguibas on Twitter
• @jtguibas on Github
• jtguibas@gmail.com via email

Bio

I am an engineer based in San Francisco. I am currently the co-founder and CTO of Succinct, where we are building an open-source RISC-V virtual machine that generates zero-knowledge proofs for any computation.

I got nerdsniped by deep learning in high school, which led to publishing at NeurIPS and joining the first batch of AI Grant. As an undergrad at Stanford, I worked on making machine learning analytics practical at the DAWN Lab, then moved to NVIDIA Research where I developed foundation models for vision and explored efficient architectures using Fourier transforms. I dropped out for the Thiel Fellowship to start Succinct.

I'm interested in thinking in exponentials and how technology can be used to benefit humanity.

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.

• I Zhang, K Kulkarni, T Li, D Wong, T Kim, J Guibas, U Roy, B Pellegrino, et al.
  vApps: Verifiable Applications at Internet Scale.
  arXiv preprint arXiv:2504.14809, 2025.
• D Kang, J Guibas, P Bailis, T Hashimoto, Y Sun, M Zaharia.
  Data Management for ML-based Analytics and Beyond.
  Journal of Data Science (JDS), 2023.
• D Kang, J Guibas, PD Bailis, T Hashimoto, M Zaharia.
  TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data.
  International Conference on Management of Data (SIGMOD), 2022.
• J Guibas, M Mardani, Z Li, A Tao, A Anandkumar, B Catanzaro.
  Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers.
  International Conference on Learning Representations (ICLR), 2022.
• D Kang, J Guibas, P Bailis, T Hashimoto, Y Sun, M Zaharia.
  Accelerating Approximate Aggregation Queries with Expensive Predicates.
  International Conference on Very Large Data Bases (VLDB), 2021.
• J Guibas, T Virdi, P Li.
  Synthetic Medical Images from Dual Generative Adversarial Networks.
  Conference on Neural Information Processing Systems (NIPS ML4H), 2017.