Bio


I am a Postdoctoral Scholar in AI at Stanford University, under the supervision of Prof. Mark Musen. Prior to that, I received my Ph.D. (with summa cum laude) in computer science at University of Stuttgart, Germany and the Intl. Max Plank Research School for Intelligent Systems (IMPRS-IS), under the supervision of Prof. Steffen Staab. My PhD was funded by the prestigious Marie Curie PhD Fellowship. I was also an associate member of the TrustAGI Lab at Griffith University, Australia, advised by Prof. Shirui Pan. I have published 20+ papers and/or served as PC in premier AI conferences such as NeurIPS, ICLR, KDD, ACL, WWW, EMNLP, NAACL, SIGIR, AAAI, ECAI, ISWC, etc., and received the Best Student Paper Award of ISWC’22.

I conduct research in AI and NLP with a special emphasis on advancing the representation of human knowledge (i.e., knowledge representation and knowledge engineering). In particular, I explore two core paradigms of representation:
1) Symbolic representation: Knowledge graphs, ontology, logic, semantic web, etc.
2) Neural representation: Large language models, graph embeddings.

By bridging these two forms of representations (neuro-symbolic), I aim to develop reliable and interpretable AI systems allowing for learning, reasoning, and adapting into real domains.

Honors & Awards


  • Best Dissertation Award in Computer Science, University of Stuttgart (2024)
  • Publication Prize in 2022, University of Stuttgart (2023)
  • Spotlight Scholar, International Max Plan Research School for Intelligent Systems (IMPRS) (2023)
  • Best Student Paper Award, International Semantic Web Conference (ISWC) (2022)

Professional Education


  • Ph.D., Intl. Max Plank Research School for Intelligent Systems (IMPRS-IS), Computer Science (2024)
  • Ph.D., University of Stuttgart, Computer Science (2024)

Stanford Advisors


Current Research and Scholarly Interests


AI, LLMs, Knowledge Graphs, Biomedical Ontologies

All Publications


  • HypMix: Hyperbolic Representation Learning for Graphs with Mixed Hierarchical and Non-hierarchical Structures. Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Lee, E. W., Xiong, B., Yang, C., Ho, J. C. 2024; 2024: 3852-3856

    Abstract

    Heterogeneous networks contain multiple types of nodes and links, with some link types encapsulating hierarchical structure over entities. Hierarchical relationships can codify information such as subcategories or one entity being subsumed by another and are often used for organizing conceptual knowledge into a tree-structured graph. Hyperbolic embedding models learn node representations in a hyperbolic space suitable for preserving the hierarchical structure. Unfortunately, current hyperbolic embedding models only implicitly capture the hierarchical structure, failing to distinguish between node types, and they only assume a single tree. In practice, many networks contain a mixture of hierarchical and non-hierarchical structures, and the hierarchical relations may be represented as multiple trees with complex structures, such as sharing certain entities. In this work, we propose a new hyperbolic representation learning model that can handle complex hierarchical structures and also learn the representation of both hierarchical and non-hierarchic structures. We evaluate our model on several datasets, including identifying relevant articles for a systematic review, which is an essential tool for evidence-driven medicine and node classification.

    View details for DOI 10.1145/3627673.3679940

    View details for PubMedID 40018085

    View details for PubMedCentralID PMC11867734