About Me

I am a fourth-year Computer Science Ph.D. candidate at the University of Amsterdam (INDElab), advised by Prof. Dr. Paul Groth and Dr. Klim Zaporojets, expecting to graduate in 2027.

I am on the job market, seeking Data Scientist and Machine Learning Engineer roles. Feel free to reach out at p.zhang@uva.nl or download my CV (PDF).

  • Multi-modal knowledge graphs
  • Entity linking
  • Link prediction
  • Recommendation
  • Large language models

Based in Amsterdam · open to roles across the Netherlands

The data drifts. My models keep up. Real-world data changes over time: the same person, product, or event looks different next year. I build deep learning models that fuse graph structure, text, images, and time, so that entity linking, link prediction, and recommendation stay accurate as data drifts. My methods improve over prior state of the art by up to 20% on entity linking and up to 10% on recommendation, with the largest gains exactly where standard models fail most: ambiguous, look-alike entities, where improvements reach 3x. Everything ships as open-source code with released datasets and benchmarks.

What’s next: I want to apply these methods to production-scale problems such as search and recommendation that stay reliable as catalogs and user behavior change, and data quality for growing knowledge bases.

Education & Awards & Service

Education

  • 2022 - Present: Ph.D. in Computer Science, INDElab, Faculty of Science, University of Amsterdam (UvA), the Netherlands. Supervisors: Prof. Dr. Paul Groth, Dr. Klim Zaporojets.
  • 2019 - 2022: M.Eng. in Control Engineering, Faculty of Information Technology, Beijing University of Technology (BJUT), China. Supervisor: Prof. Dr. Yong Zhang.

Awards

  • 2026: Conference travel grant for ACL 2026.
  • 2022: Outstanding Master’s Thesis, Beijing University of Technology.

Service

  • Reviewer / sub-reviewer: CIKM, ECAI, ESWC, PKDD.
  • 2020 - 2021: Teaching assistant, Data Engineering (BJUT).