Christina Ji

Christina Ji’s research is focused on building machine learning models to improve human health. In the healthcare space, she has worked on forecasting patient outcomes and modeling medical decision-making. In the drug discovery space, she has created models to generate biological structures and predict molecular properties. She is interested in advancing the frontiers of machine learning and biology. Ji received her Ph.D., master's, and bachelor's degrees in computer science from Massachusetts Institute of Technology. She values teaching, advising students, and fostering a welcoming community. In recognition of her teaching and mentorship, Ji received a Carlton E Tucker teaching award from MIT EECS and the 2024 graduate student extraordinary teaching and mentoring award from MIT School of Engineering. Before coming to Hamilton, she worked as a senior machine learning research engineer at Genesis Molecular AI.

Recent Courses Taught

Computer Science for All
Artificial Intelligence

Research Interests

Machine learning for healthcare, causal inference, statistical hypothesis testing, distribution shift, reinforcement learning, drug discovery

Distinctions

• Graduate Student Extraordinary Teaching and Mentoring Award, MIT School of Engineering (May 2024)
• Carlton E. Tucker Teaching Award, MIT EECS (May 2024)
• Featured Associate Advisor, MIT Office of the First Year (May 2019)
• Abdul Latif Jameel Fellowship for Machine Learning and Health Solutions, MIT (Sept. 2019–May 2020)

Selected Publications

• Pearl: A Foundation Model for Placing Every Atom in the Right Location. Genesis Research Team, A Dobles, N Jovic, K Leidal, P Murugan, DC Williams, D Wulsin, N Gruver, CX Ji (core contributor), K Pruegsanusak, G Scarpellini, A Sharma, W Swiderski, AN Bootsma, RS Bowen, C Chen, J Chen, MA Dämgen, B DiFrancesco, JD Fishman, A Ivanova, Z Kagin, D Li- Bland, Z Liu, I Morozov, J Ouyang-Zhang, FC Pickard IV, KS Shah, B Shor, G Monteiro da Silva, R Tal, M Tessmer, C Tilbury, C Vetcher, D Zeng, M Al-Shedivat, A Faust, EN Feinberg, MV LeVine, and M Pan. arxiv 2025.
• Ji, CX, Blecker, S., Oberst, M., et al. "Variation in First-Line Type 2 Diabetes Treatment due to eGFR and Provider Preferences: A Novel Statistical Analysis." medrxiv 2024
• Ji, CX, Alaa, AM, and Sontag, D. "Seq-to-Final: A Benchmark for Tuning from Sequential Distributions to a Final Time Point." arXiv 2024
• Ji, CX, Alaa, AM, and Sontag, D. "Large-Scale Study of Temporal Shift in Health Insurance Claims." CHIL 2023 (Oral spotlight)
• Lim, J.*, Ji, CX*, Oberst, M.*, et al. "Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance." NeurIPS 2021 *equal contribution

Appointed to the Faculty

2026

Educational Background

Ph.D., Massachusetts Institute of Technology
M.Eng., Massachusetts Institute of Technology
B.S., Massachusetts Institute of Technology

Dissertation

Characterizing Variation in Healthcare across Time and Providers using Machine Learning