Hyewon Jeong, M.D., M.S.
Hyewon Jeong, M.D., M.S.
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Medical Doctor, M.S. in Computer Science
Medical Doctor, M.S. in Computer Science
Brief Bio
Brief Bio
Hello! I am a Ph.D. Candidate at CSAIL, LIDS, IMES, RLE, and EECS at MIT under the supervision of Prof. Marzyeh Ghassemi and Prof. Collin Stultz. I have recently completed a master's in Computer Science at KAIST MLAI Lab (School of Computing at KAIST), doing research on Machine Learning for Health (under the guidance of prof. Sung Ju Hwang). Prior to studying at KAIST, I received a Medical Doctor (M.D.) degree from Yonsei University College of Medicine and studied Biological Sciences at KAIST.
Research Interest
Research Interest
I am broadly interested in Machine Learning for Healthcare, especially in representation learning for time series, signals, and image data. I am interested in fairness/robustness, representational learning, and causal inference.
As I was once a neuroscientist (did research under the guidance of prof. Seung-Hee Lee and prof. JaeJin Kim), I still have an interest in neuroscience as general: specifically its extension to the clinical field, neuro-inspired AI, and AI-inspired neuroscience.
Past Research Interest: I used to do research in synthetic and systems biology, and I am proud that was served as a Team leader of the KAIST-Korea team of iGEM 2012 (We are the first team from Korea who won Gold prize from iGEM! This was done under the guidance of prof. Byung Kwan Cho). While in medical school, I used to be in wet lab, doing cancer cell research! I cultured spherical cell called Glioblastoma and did research on stemness, invasiveness of the cancer stem cell (Check out my paper, which was done under the guidance of prof. Seok-Gu Kang).
News (2021~ onward)
News (2021~ onward)
2024.08.15. Our paper Event-Based Contrastive Learning for Medical Time-Series has been accepted to MLHC 2024 and we will present the poster at the conference!
2024.05.26. I’m happy to share that I’m starting a new position as a Research Intern @ Apple AIML / Health AI team!
2023. 12.10. I am serving as Research Roundtables Chair for ML4H 2023 co-located with NeurIPS 2023!
2023.11.02. We are presenting a work on Robust and fair time-to-event analysis for predicting cancer-associated Venous Thromboembolism (VTE) in ASHG 2023!
2023.10.28. An abstract on event-based contrastive learning was accepted to UniReps NeurIPS Workshop!
2023.07.18. A paper on developing ML models and identifying predictive features for Fetal Alcohol Syndrome (FAS) was published in JMIR!
2023.07.11. A paper on Deep Metric Learning for Hemodynamics inference has been accepted to MLHC! See you there in New York!
2022.10.21 Two papers have been accepted as Spotlight Posters in NeurIPS Workshops!
2022.07.22. Happy to announce that I was invited to give a talk to Stanford MedAI Journal Club
2022.03.01. A paper on Real-Time seizure detection (co-first author-ed) has been accepted to ACM CHIL 2022
2021. 09. 09. I am starting my Ph.D. studies at MIT EECS with a fellowship from JClinic! So excited to be in the Cambridge area again :D
2021. 02. 19. I graduated from the School of Computing, KAIST with a Master of Science degree :)
2021. 02. 15. I joined AITRICS as a Medical Researcher!
Education
Education
2021- Massachusetts Institute of Technology Ph.D. Student, EECS Cambridge, MA, USA
2019 – 2021 Korea Advanced Institute of Science and Technology (KAIST) M.S. Candidate, School of Computing Daejeon, Republic of Korea
2015 – 2019 Yonsei University Medical Doctor, College of Medicine Seoul, Republic of Korea
2009 – 2015 Korea Advanced Institute of Science and Technology (KAIST) Bachelor of Science in Biological Sciences, Cum Laude Daejeon, Republic of Korea
2007 – 2009 Gwangju Science High School Gwangju, Republic of Korea
hyewonjeong.contact@gmail.com
hyewonjeong.contact@gmail.com
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