Christina X Ji
I am finishing my PhD in computer science at MIT this summer. My research is in machine learning for healthcare. I completed my MEng and BS in computer science at MIT in 2019. Resume
Research
I use machine learning, causal inference, and statistics to tackle clinical questions. Some projects include:
- Benchmarking different transfer learning approaches for tuning from a sequence of distributions to the final time point
- Creating a test to detect when a model needs to be updated
- Analyzing the causal effect of choosing a particular doctor on the treatment decision
Internships
I had the opportunity to explore different kinds of problems:
- Experimented with language and diffusion models to generate molecules for specific drug targets at Genesis Therapeutics in 2023
- Ran causal inference analyses to measure the effect of LinkedIn Learning features on engagement and revenue at LinkedIn in 2021
- Built machine learning models to predict patient outcomes at Philips and IBM research in 2018
- Studied improper chromosome division in mice at the Koch Institute for cancer research from 2016 to 2017
- Wrote software to process mass spectrometry results at Janssen pharmaceuticals in 2016
Teaching
I am passionate about teaching:
- Developed and taught a 4-week class on Introduction to Statistical Hypothesis Testing (6.S098) in January 2024
- Created course material and taught recitations as a teaching assistant for Introduction to Statistical Data Analysis (6.3720) in spring 2023
- Completed workshops on subject design, lesson planning, and inclusive teaching and earned a teaching certificate from MIT teaching and learning lab
I am honored to have received the following teaching and mentoring awards:
- Carlton E Tucker teaching award from MIT EECS in 2024
- Graduate student extraordinary teaching and mentoring award from MIT School of Engineering in 2024
- Featured associate advisor for first-year undergraduates in May 2019
Community service
I also care about building a welcoming community. To help incoming students find their place at MIT:
- Organized visit days and orientation for MIT EECS PhD program from 2020 to 2022
- Mentored under-represented students on PhD applications from 2020 to 2023 through the MIT EECS graduate application assistance program
- Advised first-year undergraduates from 2016 to 2020
- Led undergraduate orientation groups from 2016 to 2018
Publications
Seq-to-final: a benchmark for tuning from sequential distributions to a final time point.
Christina X Ji, Ahmed M Alaa, and David Sontag.
Under review. 2024.
[paper] [code]
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 Horwitz, and David Sontag.
Manuscript under preparation. 2024.
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]