Yuntian He

Yuntian He works on graph representation learning, developing efficient and fair methods for analyzing large networks such as social networks and heterogeneous graphs, including techniques that work in federated (distributed, privacy-preserving) settings. Related work spans social network analysis, differential privacy, and applications to healthcare data such as sepsis prediction and clinical record processing. This research is broadly relevant to building scalable, fairness-aware machine learning tools for large, complex, real-world datasets.

Publication output has been relatively steady over the past decade, with a peak around 2021-2022 and a modest, fairly consistent pace of a few papers per year since then.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026