I am a 3rd year PhD student in computer science at MIT in the clinical machine learning group advised by David Sontag. I am interested in characterizing variation in how patients are treated, evaluating the effect of treatment policies, and handling dataset shift in predictive modeling.
I completed my MEng and BS in computer science at MIT in 2019. I interned at LinkedIn data science in summer 2021, Philips research in summer 2018, and IBM research in January 2018. I am grateful to have received the Abdul Latif Jameel fellowship for machine learning and health solutions for my first year of PhD. [CV]
I am passionate about welcoming new students to MIT. I am currently helping to build the EECS community as co-president of the EECS graduate student association. I focused on organizing visit days and orientation as co-vice president for 2 years. Before that, I led undergraduate orientation groups and advised first-year undergraduates.
If you are an MIT undergrad looking for a UROP in machine learning and healthcare, I am interested in working with you! Project description
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] [video] [code]
Modeling progression of Parkinson's disease. MEng thesis. 2019. [thesis]