Christina X Ji

I am a senior machine learning research engineer at Genesis Molecular AI. We are building machine learning models to aid in the drug discovery process. Check out the technical report we released on Pearl, a foundation model for 3D protein-ligand structure prediction!

I received my PhD, MEng, and BS in computer science from MIT. My PhD research was in machine learning for healthcare. I applied machine learning, causal inference, and statistics to analyze variation in treatment practices across providers and changes over time in healthcare.

I have experience in computer science and biology. On the tech side, I worked at Meta and did a data science internship at LinkedIn. On the biology side, I interned at Janssen pharmaceuticals and Philips healthcare, did genomics and wet lab genetics research, and took classes from biochemistry to cancer biology. My experience in both of these fields helps me contribute at their intersection.

In grad school, I enjoyed teaching and building a welcoming community. I was a TA for Introduction to Statistical Data Analysis and taught a 4-week class on Introduction to Statistical Hypothesis Testing. I earned a teaching certificate from MIT's teaching and learning lab. I also organized visit days and orientation for the MIT EECS PhD program and mentored students on their PhD applications. I 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.

Feel free to reach out on LinkedIn or at cji at alum dot mit dot edu.

Teaching

Introduction to statistical hypothesis testing (MIT 6.S098)
Instructor. Independent activities period (January) 2024.
[syllabus] [session 1 exercises]

Introduction to statistical data analysis (MIT 6.3720)
Teaching assistant. Spring 2023.
Guest lecturer. Causal inference. May 2023 and May 2024.
[slides] [notes]

Introduction to machine learning (MIT 6.036)
Lab assistant. Spring 2018.

Theses

Characterizing variation in healthcare across time and providers using machine learning
PhD thesis. 2024.
[thesis]

Modeling progression of Parkinson's disease
MEng thesis. 2019.
[thesis] [code]

Papers

Large-scale study of temporal shift in health insurance claims
Christina X Ji, Ahmed M Alaa, and David Sontag.
Oral spotlight at Conference on Health, Inference, and Learning (CHIL) 2023.
[paper] [poster] [code]

Finding regions of heterogeneity in decision-making via expected conditional covariance
Justin Lim*, Christina X Ji*, Michael Oberst*, Saul Blecker, Leora Horwitz, and David Sontag. *equal contribution
Neural information processing systems (NeurIPS) 2021.
[paper] [poster] [code]

Trajectory inspection: A method for iterative clinician-driven design of reinforcement learning studies
Christina X Ji*, Michael Oberst*, Sanjat Kanjilal, and David Sontag. *equal contribution
American medical informatics association (AMIA) 2021 virtual informatics summit.
[paper] [code]

Preprints

Pearl: A foundation model for placing every atom in the right location
Genesis Research Team et al.
arxiv. 2025.
[paper]

Variation in first-line type 2 diabetes treatment due to eGFR and provider preferences: A novel statistical analysis
Christina X Ji, Saul Blecker, Michael Oberst, Ming-Chieh Shih, Leora I Horwitz, and David Sontag.
medrxiv. 2024.
[paper] [code]

Seq-to-final: A benchmark for tuning from sequential distributions to a final time point
Christina X Ji, Ahmed M Alaa, and David Sontag.
arxiv. 2024.
[paper] [code]