Christina Ji
Visiting Assistant Professor of Computer Science
Christina Ji’s research is focused on building machine learning tools for interdisciplinary applications. In the healthcare and drug discovery space, she has worked on predicting patient outcomes, modeling medical decision-making, and generating biological structures. She is also interested in exploring how AI can be applied to support student learning and assist with domain-specific work. Ji received her Ph.D., master's, and bachelor's degrees in computer science from Massachusetts Institute of Technology. She enjoys teaching, advising students, and building a welcoming community. 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 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
2026Educational Background
Ph.D., Massachusetts Institute of Technology
M.Eng., Massachusetts Institute of Technology
B.S., Massachusetts Institute of Technology
