张大可，2024年毕业于芝加哥大学布斯商学院，获计量经济与统计学博士学位及工商管理硕士（MBA）学位；他此前分别于芝加哥大学与清华大学获统计学硕士学位及数学学士学位。

他的主要研究领域为统计学与机器学习方法及其在金融领域的应用。相关研究成果已接收于 Journal of Finance (JF)、Journal of the American Statistical Association (JASA)、International Conference on Machine Learning (ICML) 以及 International Conference on Learning Representations (ICLR) 等期刊及会议上。

1. Stefano Giglio, Dacheng Xiu, Dake Zhang*. "Prediction When Factors are Weak." Journal of the American Statistical Association (Forthcoming).

2. Qili Shen, Kairui Feng*, Ang He, Xuanhong Chen*, Dake Zhang*. "On the Computational Limits of AI4S-RL: A Unified ε-N Analysis." International Conference on Learning Representations (ICLR), 2026.

3. Stefano Giglio, Dacheng Xiu, Dake Zhang*. "Test Assets and Weak Factors." Journal of Finance, 2025.

4. Dake Zhang, Boxiang Lyu, Shuang Qiu*, Mladen Kolar, Tong Zhang. "Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning." International Conference on Machine Learning (ICML), 2024. [Spotlight Poster]

1. 金融风险管理

2. 信用风险计量

3. 区块链与数字货币