DeFi and FinTech
The Paradox of Just-in-Time Liquidity in Decentralized Exchanges: More Providers Can Lead To Less Liquidity
Agostino Capponi, Ruizhe Jia, and Brian Z. Zhu
Working Paper
Presented at: China International Conference in Finance 2024, Inter-Finance PhD Seminar, University of Michigan (Byrne Conference 2024), University
of Chicago (Stevanovich Center Conference 2024), University of Toronto, The Latest in DeFi Research Conference 2024, Sydney Market Microstructure Conference 2023, INFORMS 2023
Quantifying the Value of Revert Protection
Brian Z. Zhu, Xin Wan, Ciamac C. Moallemi, Dan Robinson, and Brad Bachu
Financial Cryptography and Data Security (FC 2025)
Presented at: The Science of Blockchain Conference 2025, UC Santa Barbara (FBC Summit 2025), The Latest in DeFi Research Conference 2025, Financial Cryptography 2025
What Drives Liquidity on Decentralized Exchanges? Evidence from the Uniswap Protocol
Brian Z. Zhu, Dingyue Liu, Xin Wan, Gordon Liao, Ciamac C. Moallemi, and Brad Bachu
Cryptoasset Analytics Workshop (CAAW 2025)
Presented at: University of Chicago (Stevanovich Center Conference 2025), Cryptoasset Analytics Workshop 2025, University of Sydney
Privacy-Enhanced Payment Systems
Agostino Capponi, Michael J. Lee, and Brian Z. Zhu
Working Paper
Presented at: INFORMS 2025 (forthcoming), GSU-MS FinTech Conference 2025 (forthcoming), European Finance Association 2025, Bank of Canada (ICPSS 2025), Swiss National Bank (Cryptoasset and Financial Innovation Conference 2025), CREST Future of Money Workshop 2025
Optimal Exiting for Liquidity Provision in Constant Function Market Makers
Agostino Capponi and Brian Z. Zhu
Working Paper
Presented at: SIAM Conference on Financial Mathematics and Engineering 2025, Vienna Congress on Mathematical Finance 2025, Peter Carr Conference 2024, INFORMS 2024
From Pools to Books: Modeling DeFi Liquidity in TradFi Terms
Agostino Capponi and Brian Z. Zhu
Manuscript available upon request
Presented at: Brazilian Finance Meeting 2025 (as mini-course), Advanced Mathematical Methods for Finance 2025 (as mini-course), École Polytechnique, Bloomberg Quant Seminar
Machine Learning
Unsupervised machine learning reveals slab hydration variations from deep earthquake distributions beneath the northwest Pacific
Gilbert L. Mao, Thomas P. Ferrand, Jiaqi Li, Brian Z. Zhu, Ziyi Xi, Min Chen
Communications Earth and Environment, Vol. 3, Art. 56 (2022)