I am a recently defended, sixth-year Ph.D. student in Computer Science at the University of Michigan, working in the MLD3 lab under Jenna Wiens. My research focuses on the development of novel machine learning algorithms for leveraging clinical time-series data. I am particularly focused on novel architectures that can learn temporal invariances, time series alignment, as well as adapt to time varying tasks. Our work has applications in healthcare. We focus on clinically relevant tasks that utilize the electronic health record (EHR) to improve patient care.
I received my Bachelors degree from the University of California, Los Angeles in Economics and Mathematics.
2260 Hayward Street
Ann Arbor, MI
* denotes equal contribution
Jeeheh Oh, Jenna Wiens, A Data-Driven Approach to Estimating Infectious Disease Transmission from Graphs:A Case of Class Imbalance Driven Low Homophily, ACM CHIL Workshop 2021, April 2021.
Jeeheh Oh*, Jiaxuan Wang*, Shengpu Tang, Michael Sjoding, Jenna Wiens, Relaxed Weight Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series, Machine Learning for Healthcare Conference (MLHC), August 2019. Link Code
Jeeheh Oh, Jiaxuan Wang, Jenna Wiens, Learning to Exploit Invariances from Clinical Time-Series Data using Sequence Transformer Networks, Machine Learning for Healthcare Conference (MLHC), August 2018. Link Code
Jeeheh Oh*, Maggie Makar* et al., A Generalizable, Data-Driven Approach to Predict Daily Risk of Clostridium difficile Infection at Two Large Academic Health Centers, Infection Control and Hospital Epidemiology (ICHE), March 2018. Link Code
Jeeheh Oh, Evan Snitkin, Vincent Young, Data-Driven Tools to Curb the Spread of Healthcare-Associated Infections, MCubed Symposium, November 2017. Video