Intae Moon

itmoon@mit.edu

Hello! 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). Since Fall 2019, I have been advised by Professor Alexander Gusev at the Dana-Farber Cancer Institute and Harvard Medical School. 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.

My PhD 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. I'm interested in demystifying disease mechanisms as well as prognosis, and ultimately improving clinical decision making.

CV / LinkedIn / Google Scholar / GitHub / Twitter

Announcements

6/15/22 : I’m excited to share that I won the 2022 Carlton E. Tucker Award for teaching excellence, thanks to the fantastic teaching staff and students of Machine Learning for Healthcare at MIT. All teaching materials are available at this link.
5/30/22 : I’m excited to announce that I'll be working with Health AI team, Google Research over Summer 2022.
5/22/22 : I’ll be presenting "Utilizing Electronic Health Records (EHR) and Tumor Panel Sequencing to Demystify Prognosis of Cancer of Unknown Primary (CUP) patients" in the poster session (5/23/22) at Symposium on Artificial Intelligence for Learning Health Systems (SAIL). We'll post the pre-print soon.
4/20/22 : I’m excited to share our work on a new generative, Neural ODE based time-to-event model for longitudinal data with competing risks, SurvLatent ODE. See here for the pre-print.

Selected Publications

Journal & Conference

SurvLatent ODE: A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Deep Vein Thrombosis (DVT) prediction

Intae Moon, 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

Intae Moon, Sylvan C. Baca, Kenneth L. Keh, 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

Intae Moon, 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

Intae Moon, 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, Intae Moon, and David J. Perreault

IEEE 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

Intae Moon, 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

Teaching

In Spring 2022, I taught 6.871 : Machine Learning for Healthcare at MIT as a teaching assistant. I held a weekly recitation session, mentored students' class projects, and developed problem sets.
In Spring 2019, I was a TA for 6.004 : Computation Structures at MIT. I helped design and build new laboratory assignments and course materials in a RISC-V assembly and hardware description language called Bluespec.