Intae Moon

Hello! 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

Announcements

6/10/24: I'm excited to announce that I have started my postdoctoral research fellowship at Harvard Medical School, Department of Biomedical Informatics, advised by Professor Marinka Zitnik.
5/2/24: I'm excited to share that I've successfully defended my PhD thesis, titled "Reliable and Trustworthy AI for Evidence-based Clinical Decision Support in Cancer Care". Check out the presentation recording here.
11/4/23: I presented our recent work titled "Robust and fair time-to-event framework for predicting cancer-associated Venous Thromboembolism (VTE) using routinely-collected clinical and panel-sequencing data" at American Society of Human Genetics (ASHG) 2023 in Washington DC! See our extended abstract and poster below.
8/7/23: I’m excited to share that our work "Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary" has been published in Nature Medicine!
5/9/23: I’m happy to announce that I will be presenting our recent work SurvLatent ODE (see paper and poster) as a poster at Symposium on Artificial Intelligence for Learning Health Systems (SAIL) conference in Puerto Rico!
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

Please see my Google Scholar page for a full list of my publications.

Teaching

Awards