I graduated with highest distinction from the University of Virginia with a Bachelor’s degree in Computer Science and Mathematics. During my undergraduate study, I worked with Professors David Wu and Yuan Tian on scalable privacy-preserving machine learning, and with Professors Haifeng Xu and Xiaohui Bei on handling miscalibration in peer reviews.
My research lies in cryptography, machine learning, and distributed systems. I am mainly interested in research in the following two directions: 1) build systems that can protect users’ data privacy by leveraging cryptographic tools. 2) studying cryptographic primitives that can improve the scalability, verifiability, and privacy of blockchain.
|Sep 26, 2021||Our paper Least Square Calibration in Peer Reviews is accepted to appeear at NeurIPS2021.|
|Jul 14, 2021||I give a talk on my research work CryptGPU at Stanford Security Lunch.|
|Feb 19, 2021||Our paper CryptGPU: Fast Privacy Preserving Machine Learning on the GPU is accepted to appear at IEEE S&P2021.|
|May 19, 2020||I joined FAIR NY working on building and scaling CrypTen with GPU.|
- IEEE S&PCryptGPU: Fast Privacy Preserving Machine Learning on the GPUIEEE Symposium on Security and Privacy (S&P), 2021
- NeurIPSLeast Square Calibration for Peer ReviewsConference on Neural Information Processing Systems (NeurIPS) 2021