About Me

I am a Ph.D. student in the Yale Applied Cryptography Lab, working with Ben Fisch. My current research focuses on recursive zero-knowledge proofs, which have powerful applications in decentralized systems and machine learning.

I received a B.S. and M.S. in Computer Science from Stanford University. I worked with Fei-Fei Li and Serena Yeung in the Stanford Vision and Learning Lab.

At Pinterest, I have led a variety of large-scale machine learning projects and published research on representation learning, object detection, and recommender systems.

I was the co-founder and CTO of Reveal, a venture-backed startup that developed one of the first social networking apps with cryptocurrency incentive mechanisms.

Selected Publications

For a complete list of publications, please visit my Google Scholar profile here.

• Mira: Efficient Folding for Pairing-Based Arguments
  Josh Beal, Ben Fisch. ePrint 2024.
• Derecho: Privacy Pools with Proof-Carrying Disclosures
  Josh Beal, Ben Fisch. CCS 2024.
• Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations
  Josh Beal, Hao-Yu Wu, Dong Huk Park, Andrew Zhai, Dmitry Kislyuk. WACV 2022.
• Toward Transformer-Based Object Detection
  Josh Beal, Eric Kim, Eric Tzeng, Dong Huk Park, Andrew Zhai, Dmitry Kislyuk. arXiv 2020.
• Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference
  Josh Beal, Edward Chou, Daniel Levy, Serena Yeung, Albert Haque, Li Fei-Fei. arXiv 2018.