Projects

Historically, my interests have spanned mathematics (real analysis, probability theory, statistics), computer systems (distributed systems, CUDA, systems programming), cryptography, and machine learning. I've spent the past few years mostly writing Rust and C++.

Projects

For a full list of projects, please refer to my GitHub.

• SP1: a zero-knowledge virtual machine that proves the correct execution of programs compiled for the RISC-V architecture.
• Succinct Prover Network: a protocol on Ethereum that coordinates a distributed network of provers to generate zero knowledge proofs.

Papers

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