Research

My research lies at the intersection of neural and symbolic AI, integrating advances in deep learning and large language models (LLMs) with structured knowledge representations including knowledge graphs and ontologies. I develop AI systems that are not only powerful in prediction but also reliable, interpretable, and robustly generalizable by design, ensuring that they can be trusted in high-stakes domains such as biomedicine and healthcare.

To achieve this, I conduct fundamental research in the following areas:

Knowledge-enhanced LLMs/foundation models

Integrating knowledge with large language models to improve their reliability, interpretability, factual accuracy, or generalization ability
Representative work: ICLR’25, EMNLP’25, NAACL’25, EMNLP’24, NeSy’25

Foundation models for structured data – Developing generalizeble models for structured data
Representative work: EMNLP’25, ICML’25

Machine learning with data & knowledge structures

Learning on graph-structured data — Developing methods for learning from graph-structured information, including knowledge graphs and ontologies.
Representative work: ICML’25, WWW’25, NeurIPS’22a, ACL’23, AAAI’24, CIKM’24, KDD’22

Semantic web and ontologies — Working with description logic and ontology reasoning for structured knowledge representation.
Representative work: ISWC’22, ISWC’23

Geometric representation learning — Exploiting data geometry (hyperbolic, pseudo-Riemannian spaces) to improve machine learning on hierarchical and complex-structured data.
Representative work: NeurIPS’22a, NeurIPS’22b, KDD’22

Interpretable and Reliable AI

Neuro-symbolic learning — Combining neural networks with symbolic reasoning by imposing structure and prior knowledge in machine learning models.
Representative work: NeurIPS’22b, ISWC’22, ICDE’24

ML for healthcare and biomedicine — Applying machine learning techniques to medical and healthcare domains.
Representative work: ICML’25, AMIA’24, SIGIR’23