Grace (Xiyu) Ding
Federated Bayesian models for health equity
PhD Candidate
Johns Hopkins University
Your week at a glance
- Small group — you facilitate Group 8 · Draco on Day 1 (Problem framing).
- Job talk — Day 6 (Sat, Jun 27), 4:00 PM · Room A · Alder 107 — “High-dimensional Bayesian Transfer Learning in Federated Settings”.
- Poster — Day 7 (Sun, Jun 28) · Session A (9:15–10:15 AM).
Grace (Xiyu) Ding is a 5th year PhD in Biomedical Informatics and Data Science at Johns Hopkins University. Her research focuses on developing data-driven methods to improve healthcare delivery, equity, and real-world clinical decision-making. Her work spans Bayesian modeling, federated analytics, natural language processing, and multimodal modeling. Prior to Johns Hopkins, Grace earned an M.S. in Computational Biology from Harvard University and a B.S. in Biology from Nanjing University (China). Outside of research, she likes hiking, traveling, cooking and watercolor painting, and she tries to convince Duolingo that she really is committed to learning Spanish.
Awards & honors
Recognition
- Global Biotech Revolution 'Leader of Tomorrow' (1 of 100 selected globally) (2025)
- Distinguished Paper Award, AMIA 2025 Annual Symposium (2025)
- Distinguished Poster Nomination, AMIA 2020 Annual Symposium (2020)
- Hackathon Track Winner (AI in Healthcare), Harvard Hacking Public Health Competition (2019)
- Health Equity and Leadership Award, Harvard Hacking Public Health Competition (2019)
- Excellent Graduate Award, Nanjing University (2018)
- Honorable Mention, Interdisciplinary Contest in Modeling (2017)
- Roche Diagnostics China Medical & Life Science Educational & Research Fund Award (2017)
- Excellent Student Award, Nanjing University (2015-2016)
- People's Scholarship Award, Nanjing University (2015-2016)