Education
2018-2025 Beihang University (BUAA), Beijing, China
School of Computer Science and Engineering
Ph.D. in Computer Software and Theory
GPA 3.76/4, ranked 11/91 (12%)
Supervised by: Prof. Wenfei Fan and Dr. Yaoshu Wang
Academic Research during Ph.D. (slide)
2014-2018 Xidian University (XDU), Xi’an, China
School of Computer Science and Technology
B.E. in Computer Science and Technology
GPA 3.74/4, ranked 3/175 (1.7%)
Research Interests
- Data Mining/Rule Discovery: Sampling, Top-k, Diversified, etc.
- Logic Deduction combined with ML models: AI for DB, DB for AI, etc.
- Data Management: Error Detection, Data cleaning, etc.
- Data Quality: Conflict resolution, Entity resolution, Tuple splitting, etc.
My research interests include data quality, data management, rule discovery, and other areas of combining DB and AI. I have a strong interest in the intersection of DB and AI. I am particularly intrigued by how database techniques can enhance AI model performance, and vice versa. I expect to explore applying logic rules to make black-box ML models more interpretable and robust, collaborative optimization between logic rule discovery and specific downstream ML tasks, utilizing ML techniques to optimize various database management processes, etc.
I am also open to exploring other research directions and enthusiastic about investigating new areas of interest.
Currently Job Searching (actively seeking new opportunities).Publications
(Note: In papers 1-4, all authors are sorted by alphabetic order.)
- Wenfei Fan, Ziyan Han, Min Xie, and Guangyi Zhang. Discovering Top-k Relevant and Diversified Rules. In SIGMOD (2025). ACM.
- Wenfei Fan, Ziyan Han, Weilong Ren, Ding Wang, Yaoshu Wang, Min Xie, and Mengyi Yan. Splitting Tuples of Mismatched Entities. In SIGMOD (2024). ACM.
- Wenfei Fan, Ziyan Han, Yaoshu Wang, and Min Xie. Discovering Top-k Rules using Subjective and Objective Criteria. In SIGMOD (2023). ACM.
- Wenfei Fan, Ziyan Han, Yaoshu Wang, and Min Xie. Parallel Rule Discovery from Large Datasets by Sampling. In SIGMOD (2022). ACM.
- Ting Deng, Lei Hou, and Ziyan Han. Keys as features for graph entity matching. In ICDE (2020). IEEE.
Research Experiences
My research primarily focuses on data mining, rule discovery, and logic deduction combined with machine learning models, specifically on the discovery and application of data quality rules. My work has been published in top-tier database conferences, i.e., SIGMOD and ICDE. Below is a concise overview of my contributions across various domains.
- Data Mining and Data Analysis
- I have tackled several challenges in rule discovery, including high computational costs and extensive resource consumption [SIGMOD22], the limitations of non-comprehensive rule evaluation metrics that lack subjective criteria [SIGMOD23], and redundancy within mined rule sets [SIGMOD25].
- Data Management and Data Quality
- I have developed methods for resolving conflicts within tuples of mismatched entities [SIGMOD24], and for graph entity resolution using graph keys [ICDE20].
- Logic Deduction combined with Machine Learning Models
- I have integrated machine learning techniques with logic rules to enhance data quality. Specifically, I utilize machine learning techniques to accelerate the rule discovery process [SIGMOD22, SIGMOD23, SIGMOD25]. Additionally, rules discovered can be further applied to improve data quality, such as entity resolution, conflict resolution, and tuple splitting [SIGMOD24, ICDE20].
- I have integrated machine learning techniques with logic rules to enhance data quality. Specifically, I utilize machine learning techniques to accelerate the rule discovery process [SIGMOD22, SIGMOD23, SIGMOD25]. Additionally, rules discovered can be further applied to improve data quality, such as entity resolution, conflict resolution, and tuple splitting [SIGMOD24, ICDE20].
Working Experiences
2021-2024 Intern Researcher, Shenzhen Institute of Computing Sciences, Shenzhen, China
- Project: Parallel Rule Discovery from Large Datasets by Sampling [link]
- Project: Discovering Top-k Rules using Subjective and Objective Criteria [link]
- Project: Discovering Top-k Relevant and Diversified Rules [link]
- Project: Splitting Tuples of Mismatched Entities [link]
2019-2020 Research Assistant, Beihang University, Beijing, China
- Project: Keys as features for graph entity matching [link]
2020-2021 Teaching Assistant, Beihang University, Beijing, China
- Class: Formal Languages and Automata
Awards
2018 – 2025 Beihang University
- Distinguished Graduate of Beihang University, 2025
- SIGMOD 2025 Student Travel Grants, 2025
- SIGMOD 2024 Student Support Scholarship, 2024
- SIGMOD 2023 Student Travel Award, 2023
- Outstanding Freshman Scholarship, BUAA, 2018
- Outstanding Graduate Students Award, BUAA, 2020
- Merit Student Award, BUAA, 2019/2020/2021
- The Second Prize Scholarship, BUAA, 2019/2021
- The First Prize Scholarship, BUAA, 2020
- CASC Scholarship, BUAA, 2022
2014 – 2018 Xidian University
- National Scholarship for Encouragement, 2016
- The Special Scholarship, XDU, 2017
- National/Provincial College Student Innovation and Entrepreneurship Training Program Completion Certificate, 2017
- Outstanding Student Model Award, XDU, 2017
- The Second Prize Scholarship, XDU, 2015
- The Second Prize Scholarship, XDU, 2014
Skills & Hobbies
- Programming: Python, Java, Bash, C/C++, Markdown
- Tools: LaTeX, Git, Spark
- Languages: English, Chinese (native)
- Hobbies: Tennis, Fitness (Strength Training), Cooking, Latin Dance, etc.
Services
- External Reviewer: AAAI 2023, APWEB 2023, TKDE 2023, ICDE 2024, ICDE 2025, APWEB 2025, WAIM 2025
- Volunteer: SIGMOD 2023