Christina X Ji

I am a senior machine learning research engineer at Genesis Therapeutics. We are building machine learning models to aid in the drug discovery process!

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. I hope to contribute more at the intersection of these fields.

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 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 a 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.

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

Assessing variation in first-line type 2 diabetes treatment across eGFR levels and providers.
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]