CV

Education

The Ohio State University

Columbus, Ohio, U.S. | Ph.D. in Computer Science and Engineering | Expected 2029

• Advisor: John Paparrizos

Beijing University of Technology

Beijing, China | B.S. in Statistics, Minor in Computer Science and Technology | 2024

• GPA: 3.93/4.0, Rank: 1/26

Research Experience

Ph.D. Student, Department of Computer Science and Engineering, The Ohio State University

Aug. 2024 – Present

• Working on time-series anomaly detection, with a focus on hierarchical, scalable, and interpretable methods.
• Designed and implemented a hierarchical reference-based detection framework for anomalies across multiple temporal resolutions.
• Built end-to-end experimental pipelines and benchmarked methods on large-scale anomaly detection datasets.
• Developed interactive tools for interpreting anomaly scores and understanding model behavior.

Research Assistant, School of Artificial Intelligence, Beijing University of Posts and Telecommunications

Mar. 2024 – Aug. 2024

• Worked on physics-guided 3D reconstruction and wild inverse rendering with polarization and neural fields.
• Simplified a polarization-based imaging model under strong illumination for robust surface normal recovery.
• Combined classical photometric stereo constraints with neural rendering methods for outdoor inverse rendering.
• Improved reconstruction quality under complex lighting and geometry conditions.

Undergraduate Researcher, Institute of Applied Probability and Statistics, Beijing University of Technology

Oct. 2022 – May 2024

• Used Gaussian processes as surrogate models for complex computer experiments.
• Proved mathematically that adding a variance term to the loss function can reduce fluctuation while maintaining accuracy.
• Reproduced KO and GOLS calibration methods using data from the MATLAB FEM toolbox.
• Applied GOLS calibration to stochastic computer simulation.

Undergraduate Researcher, Tsinghua Statistical Artificial Intelligence & Learning Group

Mar. 2023 – Aug. 2023

• Compiled core research papers in continual learning and contributed to an open-source paper list on GitHub.
• Benchmarked HiDe-Prompt against multiple prompt-based continual learning baselines.
• Conducted hyperparameter tuning, recorded experimental results, visualized outputs, and improved code efficiency.

Work Experience

Data Analyst Intern, JD Research Institute for Consumption and Industrial Development

Beijing, China | Jun. 2022 – Aug. 2022

• Analyzed consumer behavior and market trends using web crawling, time-series methods, and machine learning.
• Supported projects related to gifting behavior, fitness equipment markets, and rural home appliance replacement policies.

Quant Intern, GuoTai Asset Management Co., Ltd., Active Management Department

Beijing, China | Jun. 2023 – Aug. 2023

• Worked on stock trend prediction and quantitative investment using deep learning and multi-task learning.
• Generated alpha factors, conducted backtesting, and analyzed financial text with pretrained language models.

Publications