Christina X Ji

Christina

I am a final-year PhD student in computer science at MIT. My research is focused on machine learning for healthcare. I completed my MEng and BS in computer science at MIT in 2019.

In my research, I use machine learning, causal inference, and statistics approaches to tackle clinical questions. Some projects include analyzing the causal effect of choosing a particular doctor on the treatment decision, creating a test to detect when a machine learning model is affected by distribution shift over time, and developing transfer learning approaches to adapt to such shifts.

As an intern at Genesis Therapeutics in 2023, I worked on machine learning for drug discovery. When I interned at LinkedIn in 2021, I ran causal inference analyses to measure the effect of LinkedIn Learning features. During undergrad, I interned at Philips healthcare, IBM research, Koch Institute for cancer research, and Janssen pharmaceuticals.

Outside of research, I am passionate about teaching. This January, I developed and taught a 4-week class on Introduction to Statistical Hypothesis Testing (6.S098). The class was inspired by Introduction to Statistical Data Analysis (6.3720), for which I was a teaching assistant in spring 2023. To learn more about good teaching practices, I completed workshops on subject design, lesson planning, and inclusive teaching through the MIT teaching and learning lab.

I also care about building a welcoming community. To help new students find their place at MIT, I organized many visit days and orientation events for the MIT EECS department from 2020 to 2022. I mentored under-represented students on their PhD applications from 2020 to 2023 through the MIT EECS graduate application assistance program. Before that, I led undergraduate orientation groups and advised first-year undergraduates.

I would be happy to hear from people at cji at mit dot edu or at my Twitter or LinkedIn!
[Resume]

Publications

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] [video] [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] [video] [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] [video] [code]

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

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