Intae MoonHello! My name is Intae (pronounced IN-teh) and I'm a postdoctoral research fellow at Harvard Medical School, Department of Biomedical Informatics, advised by Professor Marinka Zitnik. I completed my PhD in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) in 2024. I was advised by Professor Alexander Gusev at the Dana-Farber Cancer Institute and Professor Marzyeh Ghassemi at CSAIL, MIT. For my master's thesis, I was advised by Professor David Perreault at the MIT Research Laboratory of Electronics (RLE), where I worked on electrical energy conversion and control. I received my B.S. in Electrical and Computer Engineering from the University of Illinois, Urbana-Champaign. My research lies at the intersection of medicine and AI, focusing on harnessing the depth of biomedical data to inform evidence-based clinical decision support, while also mitigating potential harms from biased AI algorithms. CV / LinkedIn / Google Scholar / GitHub / Twitter |
Robust and fair time-to-event framework for predicting cancer-associated Venous Thromboembolism (VTE) using routinely-collected clinical and panel-sequencing data
, Hyewon Jeong, Alexander Gusev, and Marzyeh Ghassemi
American Society of Human Genetics (ASHG), 2023: [extended abstract], [poster]
Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary
, Jaclyn LoPiccolo, Sylvan C. Baca, Lynette M. Sholl, Kenneth L. Kehl, Michael J. Hassett, David Liu, Deborah Schrag, and Alexander Gusev
Nature Medicine, 2023: [manuscript], [codes]
Featured on MIT News and DFCI News!
We have also published a Research Briefing in Nature Medicine.
SurvLatent ODE: A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction
, Stefan Groha, and Alexander Gusev
Proceedings of Machine Learning Research (PMLR), Machine Learning for Healthcare 2022:
Utilizing Electronic Health Records (EHR) and Tumor Panel Sequencing to Demystify Prognosis of Cancer of Unknown Primary (CUP) patients
, Sylvan C. Baca, Kenneth L. Kehl, and Alexander Gusev
Symposium on Artificial Intelligence for Learning Health Systems (SAIL), 2022: [abstract]
A high-performance 65 w universal ac-dc converter using a variable-inverter-rectifier-transformer with improved step-down capability
, Mike K. Ranjram, Sombuddha Chakraborty, and David J. Perreault
IEEE Energy Conversion Congress and Exposition (ECCE) 2019: [paper link]
A wide operating range converter using a variable-inverter-rectifier-transformer with improved step-down capability
, Mike K. Ranjram, Sombuddha Chakraborty, and David J. Perreault
IEEE Applied Power Electronics Conference and Exposition (APEC) 2019: [paper link]
Variable-inverter-rectifier-transformer: A hybrid electronic and magnetic structure enabling adjustable high step-down conversion ratios
Mike K. Ranjram,
, and David J. PerreaultIEEE Transactions on Power Electronics 2018: [paper link]
Design and implementation of a 1.3 kW, 7-level flying capacitor multilevel AC-DC converter with power factor correction
, Carl F Haken, Erik K Saathoff, Ethan Bian, Yutian Lei, Shibin Qin, Derek Chou, Steven Sedig, Won Ho Chung, and Robert CN Pilawa-Podgurski
IEEE Applied Power Electronics Conference and Exposition (APEC) 2017: [paper link]