Intae Moon

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). 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.

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.

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Announcements

12/27/22 : I’m excited to share our work "Utilizing Electronic Health Records (EHR) and Tumor Panel Sequencing to Demystify Prognosis of Cancer of Unknown Primary (CUP) patients". See here for the pre-print.
10/25/22 : I’m excited to announce that I've been selected as a predoctoral semifinalist for the 2022 Charles J. Epstein Trainee Awards for Excellence in Human Genetics Research, presented by the American Society of Human Genetics (ASHG). 60 semifinalist and 18 finalist were chosen among over 700 applicants.
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 Google Health AI team over Summer (full-time) and Fall 2022 (part-time).
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

Teaching