About Me 👩🔬
Hi! I am a Ph.D. student studying Biostatistics at the Harvard T.H. Chan School of Public Health. I am interested in developing interpretable and reliable deep learning methods for digital phenotyping and precision health applications, with a larger goal of democratizing health. Specifically, I am interested in analyzing continuously and passively collected data from personal digital devices (e.g. smartphones and wearables) to quantify and improve the quality of life of individuals suffering from cancer, mental health disorders, and other diseases.
I am very fortunate to be advised by Prof. Jukka-Pekka Onnela. My Ph.D. is generously supported by the Harvard Prize Fellowship and the NIH T32 Cancer Training Grant.
Previously, I obtained a master’s degree in Operations Research and Financial Engineering from Princeton University. Before my master’s, I worked as a Lab Manager at Columbia Zuckerman Mind Brain Behavior Institute. I completed a bachelor’s degree in Mathematics from Columbia University.
[CV, github, Google Scholar]
Publications and Preprints 📝
* indicates equal contribution
- Cui, S., Yoo, E. C., Li, D., Laudanski, K., & Engelhardt, B. E. (2021). Hierarchical Gaussian Processes and Mixtures of Experts in Predicting COVID Patient Trajectories. In Proceedings of Pacific Symposium on Biocomputing 2022. [paper]
- Mandyam, A.*, Yoo, E. C.*, Soules, J.*, Laudanski, K., & Engelhardt, B. E. (2021). COP-E-CAT: cleaning and organization pipeline for EHR computational and analytic tasks. In Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 1-9). [paper]
- Demers, M. F.*, Ianzano, C. J.*, Mayer, P.*, Morfe, P.*, & Yoo, E. C.* (2017). Limiting distributions for countable state topological Markov chains with holes. Discrete & Continuous Dynamical Systems, 37(1), 105. [paper]
Personal 🧗
In my free time, I like to rock climb.
I also enjoy hiking, seeking new dessert spots, and watching anime.
