Hyeonwoo Lee
Machine Learning Engineer
I have expertise in research, application, and libraries of computer vision and deep learning.
My current focus is on applying machine learning/deep learning algorithms for healthcare applications.
I have developed detection, segmentation, classification, registration, and generative models for various modalities including MR, ultrasound, and microscopic images.
I am passionate about expanding my knowledge in personal healthcare and machine learning software development.
Donwload Resume
Current Position
Research Data Scientist
Philips Research
Ultrasound AI group
Philips Research
Ultrasound AI group
Education
Master in Biomedical Engineering, 2019
Cornell University
Bachelor in Biomedical Engineering, 2017
University of Rochester
Cornell University
Bachelor in Biomedical Engineering, 2017
University of Rochester
Professional Experiences
July 2019 - Current
Research Data Scientist
Philips,
Philips Resarch, Ultrasound AI group
- Development and application of machine/deep learning algorithms to biomedical and physiological data
- Leading deep learning algorithm development for FAST ultrasound exam on mobile ultrasound system: Lumify
July 2018 - July 2019
Scientific Data Engineer
Allen Institute for Cell Science, Image-based Assay Development team
- Developed computer vision open source toolkit for microscopic cell images, Allen Cell Structural Segmenter
- Researched & Developed deep learning based object detection, segmentation and gernative models
Recent Updates
Oct 2023
Patent in ultrasound in guidance is published in USPTO
May 2023
Our two-stage feature detection for FAST exam gudiance is accepted to IEEE IUS
May 2023
AI-based FAST Exam guidance was presented at Society for Academic Emergency Medicine (SAEM)
Dec 2022
Our work in automoated cerebral vessel detection is presented at International Symposium on Intracranial pressure and Brain Monitoring (ICP)
Oct 2022
Presented AI-based organ detection in ultrasound FAST exam at American College of Emergency Physicians (ACEP)
Sep 2022
AI Assistance to Acquire High-Quality FAST Exams is presented at Military Health System Research Symposium (MHSRS)
May 2022
Promoted to Data Scientist - AI Based Medical Ultrasound Imaging Analysis
June 2021
Co-authored research by developing cardiomyocytes segmentation model, Cell states beyond transcriptomic is publisehd to Cell Systems
Apr 2021
Our work in Automated ultrasound methods for cerebral blood flow velocity measurement is presented at Point of Care Ultrasound conference
Dec 2020
Allen Cell and Structure Segmenter is published to bioRxiv
July 2020
Started working as Assocaite Data Scientist at Ultrasound Application Group, Precision Diagnosis and Image-Guided Therapy, Philips Research
Dec 2019
Check out my Master's research on Semi-Supervised Brain MR Image Segmentation at Neurips 2019 ML4H
July 2019
Started my professional career as Scientific Data Engineer at Allen Institute!
May 2019
Graduated from Cornell University with Master in Biomedical Engineering