Scholar
Hyewon Jeong
Google Scholar ID: wkzH1QYAAAAJ
M.D., Ph.D. Candidate @ EECS, MIT
Citations & Impact
Citations
471
H-index
8
i10-index
7
Publications
20
Co-authors
0
Contact
Publications
Resume (English only)
Academic Achievements
- - 2024.08.15: Paper 'Event-Based Contrastive Learning for Medical Time-Series' accepted to MLHC 2024
- - 2023.11.02: Presenting work on Robust and fair time-to-event analysis for predicting cancer-associated Venous Thromboembolism (VTE) at ASHG 2023
- - 2023.10.28: Abstract on event-based contrastive learning accepted to UniReps NeurIPS Workshop
- - 2022.10.21: Two papers accepted as Spotlight Posters in NeurIPS Workshops
- - 2021.09.09: Started Ph.D. studies at MIT EECS with a fellowship from JClinic
- - 2021.02.19: Graduated from the School of Computing, KAIST with a Master of Science degree
Research Experience
- - 2024.05.26: Research Intern at Apple AIML / Health AI team
- - 2023.12.10: Research Roundtables Chair for ML4H 2023 co-located with NeurIPS 2023
- - 2023.07.18: Published a paper on developing ML models and identifying predictive features for Fetal Alcohol Syndrome (FAS) in JMIR
- - 2023.07.11: A paper on Deep Metric Learning for Hemodynamics inference accepted to MLHC
- - 2022.07.22: Invited to give a talk at Stanford MedAI Journal Club
- - 2022.03.01: A paper on Real-Time seizure detection (co-first author) accepted to ACM CHIL 2022
- - 2021.02.15: Joined AITRICS as a Medical Researcher
Education
- - 2021-Present: Massachusetts Institute of Technology (MIT), Ph.D. Student, EECS, supervised by Prof. Marzyeh Ghassemi and Prof. Collin Stultz
- - 2019-2021: Korea Advanced Institute of Science and Technology (KAIST), M.S. Candidate, School of Computing, supervised by Prof. Sung Ju Hwang
- - 2015-2019: Yonsei University, Medical Doctor, College of Medicine
- - 2009-2015: Korea Advanced Institute of Science and Technology (KAIST), Bachelor of Science in Biological Sciences, Cum Laude
- - 2007-2009: Gwangju Science High School
Background
- Medical Doctor, M.S. in Computer Science. Broadly interested in Machine Learning for Healthcare, especially representation learning for time series, signals, and image data. Interested in fairness/robustness, representational learning, and causal inference. Former neuroscientist with an interest in neuroscience, its extension to the clinical field, neuro-inspired AI, and AI-inspired neuroscience.
Miscellany
- Personal interests include synthetic and systems biology. Served as the Team leader of the KAIST-Korea team of iGEM 2012, which won the Gold prize.
Co-authors