About Me

I am currently a second-year M.S. student at the University of Michigan - Ann Arbor, specializing in Electrical and Computer Engineering. I am also a member of the DAIR Lab at Peking University, where I am advised by Prof. Bin Cui and Prof. Xinyi Zhang. Our work focuses on leveraging a CDF-based framework to generate a synthetic database from exceptionally large query workloads, handling up to \(10^8\) queries. This project is nearing completion and is anticipated for submission to VLDB 2025. (Further details will be shared soon.)

Before joining the University of Michigan, I earned a B.Eng. degree in Software Engineering from Peking University in 2023. Under the supervision of Prof. Qixun Zhang, I developed a direction-based clustering method to effectively address false positive noise in AIS (Automatic Identification System) data. Prior to that, in 2021, I received a B.S. degree in Information and Computational Science from Wuhan University, where I worked under the supervision of Prof. Xiliang Lv to develop a Python Remez approximation solver. This tool efficiently supports approximations of up to order 33, achieving an exceptional error of (10^{-14}) over any given interval. Its broad applicability enables it to handle a wide variety of mathematical functions, outperforming MATLAB’s built-in toolbox in both accuracy and flexibility.

Research Interests

My research interests lie at the intersection of computational problems and interdisciplinary science. Currently, my work focuses on developing and enhancing foundational numerical algorithms while leveraging scientific machine learning for database query optimization. I am particularly fascinated by how deep learning models can extract meaningful representations from data, offering a data-driven perspective to understanding scientific problems.

1. Numerical Methods and Scientific Computing
2. Algorithms, Combinatorics and Optimization (ACO)
3. Synthetic Database Generation
4. Deep Generative Model
5. Machine Learning
6. AI4Science