Intae MoonHello! My name is Intae (pronounced IN-teh) and I'm currently pursuing a PhD in Electrical Engineering and Computer Science at Massachusetts Institute of Technology (MIT). I've been advised by Professor Alexander Gusev at the Dana-Farber Cancer Institute and Harvard Medical School and Professor Marzyeh Ghassemi at CSAIL, MIT. Before starting my current PhD thesis work, I was advised by Professor David Perreault at MIT Research Laboratory of Electronics (RLE) and worked on electrical energy conversion and control for my master's thesis. I received B.S. in electrical and computer engineering from the University of Illinois, Urbana-Champaign. I'm interested in improving deployability of machine learning models by making them robust to real-world data issues such as missing data, heterogeneity, and dataset shifts in critical decision-making settings like healthcare. In terms of practical applications, my research involves developing machine learning-based approaches at the intersection of Electronic Health Records (EHR) and genomics data for improving clinical management of patients with cancer. CV / LinkedIn / Google Scholar / GitHub / Twitter |
![]() |
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, link
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, link
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, link, 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, 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, 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, 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, link