I’m currently a post-doc researcher at NVIDIA Research. Starting in the Fall of 2025, I will be an assistant professor in the Department of Computer Science at Purdue University. I received my PhD degree from the EECS Department of MIT advised by Srini Devadas and earned my B.S. degree in Mathematics from Tsinghua University.

I am broadly interested in information privacy, security and robust statistics. I endeavor to establish theory and tools for provable, interpretable and usable security and privacy exploiting statistical, information-theoretical and cryptographic methods. In particular, I focus on

1. semantic and rigorous measures which are accessible to a general audience with board expressibility of security and privacy concerns (CRYPTO 2023);
2. end-to-end automatic privacy proof and privacy-preserving technology (IEEE S&P 2025) that accommodates cutting-edge advancements in data science and machine learning (ICML 2020, PODS 2022, CCS 2024);
3. fundamental relationship between security & privacy, the minimal utility/efficiency overhead (CCS 2023, IEEE S&P 2023) and (Byzantine/Adversarial) robustness (SODA 2024) for simultaneous algorithm improvement.

My earlier works encompass a range of related topics including Byzantine consensus (TCC 2020a, TCC 2020b), and proof of work (TCC 2017) in applied cryptography, and constrained sampling theory in signal processing (IEEE-TSP 2023a, IEEE-TSP 2023b).

I was a recipient of Mathwork Fellowship (2021-2023) and Tsinghua Future Scholar Fellowship (2015-2017) . My research has also been supported and funded by DSTA, Singapore, Captical One and Cisco.

I am always looking for students who are passionate about developing useful theory to guide practical solutions. If you’re interested in working with me, feel free to send me your CV.