Faculty Directory
- Name
- June Tae KIM, Ph.D.
- Faculty Appointment
(title, department)
- Assistant Professor, Department of Cancer AI and Digital Health, NCC-GCSP / Adjunct Faculty, Healthcare AI Team
- Area of Expertise
- Methods: Probabilistic Deep Learning with a Bayesian approach, Generative Models,
Representation Learning, Deep Learning for Survival Analysis
Data : Pulsatile Physiological Signals (ABP, ECG, PPG), Genetic Data, Time-to-Event Data
- E-mail
- june.kim@ncc.re.kr, lyjune0070@gmail.com
- Work Experience
- National Cancer Center
- Assistant Professor, National Cancer Center, Department of Cancer Control and Population Health (Oct 2019 ~ Present)
- Adjunct Faculty, National Cancer Center, Cancer Big Data Center (Oct 2019 ~ Present)
Samsung Electronic
- Staff Engineer, DS Division, Memory Business, Memory Sales & Marketing Team, Strategic Planning Group (Dec 2018 – Sep 2019)
· AI Researcher
- Staff Engineer, Samsung Electronics, DS Division, Smart IT Team, Smart Factory Group (Mar 2018 – Nov 2018)
· Strategic Planner of Information System
· AI Researcher
- Educational Background
- 2013 – 2018, Ph.D., Management Information System, Management Engineering, KAIST, Seoul, Korea
2006 – 2013, B.S., Business and Management, Hanyang University, Seoul, Korea
- Research Interests
- I am engaged in research that integrates domain-specific knowledge of pulsatile physiological signals (ABP, ECG, PPG, etc.) and genetic data with deep learning. My contributions have led to me being the principal author of two papers in the highly respected journals: IEEE Transactions on Neural Networks and Learning Systems (TNNLS) and Artificial Intelligence in Medicine. Furthermore, I have secured one domestic patent, have three patent applications pending (including one for the PCT international), and hold a software copyright, in the realm of physiological signal data. In the field of genomics, I have innovated a prognosis gene recommendation algorithm, which was presented at the prestigious International Conference on Machine Learning (ICML) in 2023, and have also filed a patent application for it. For further details and to access the codes, please visit my GitHub repository at https://github.com/JunetaeKim
- Teaching in GCSP
- Deep learning algorithm
Python
Machine learning
- Achievements
- The asterisk (*) denotes the primary author and the hash mark (#) indicates the corresponding author, respectively. Please visit my GitHub repository at https://github.com/JunetaeKim
# (2023) "Intraoperative Hypotension Prediction Based on Features Automatically Generated Within an Interpretable Deep Learning Model," IEEE Transactions on Neural Networks and Learning Systems, IF: 14.255, Online: Published.
# (2023) "Development of a Bispectral Index Score Prediction Model Based on an Interpretable Deep Learning Algorithm," Artificial Intelligence in Medicine, Vol. 143, pp. 102569, IF: 7.5.
(2023) "A Comparison of Machine Learning Models and Cox Proportional-Hazards Models Regarding Their Ability to Predict the Risk of Gastrointestinal Cancer Based on Metabolic Syndrome and Its Components," Frontiers in Oncology, Vol. 13, pp. 1049787, IF: 4.7.
(2021) "A Nomogram for Predicting Probability of Low Risk of MammaPrint Results in Women with Clinically High-Risk Breast Cancer," Scientific Reports, Vol. 11(1), Article 23509, IF: 4.38.
(2021) "The Deterrent Effect of Ride-Sharing on Sexual Assault and Investigation of Situational Contingencies," Information Systems Research, Online: Published, IF: 5.207.
* (2020) "Limitations of Deep Learning Attention Mechanisms in Clinical Research: Empirical Case Study Based on the Korean Diabetic Disease Setting," Journal of Medical Internet Research, Vol. 22(12), Article e18418, IF: 5.034.
* (2020) "Understanding Time Series Patterns of Weight and Meal History Reports in Mobile Weight Loss Intervention Programs: Data-Driven Analysis," Journal of Medical Internet Research, Vol. 22(8), Article e17521, IF: 5.034.
* (2020) "Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study," JMIR Medical Informatics, Vol. 8(3), Article e16349, IF: 2.577.
(2018) "A Nomogram for Predicting the Oncotype DX Recurrence Score in Women with T1-3N0-1miM0 Hormone Receptor-Positive, Human Epidermal Growth Factor 2 (HER2)-Negative Breast Cancer," Cancer Research and Treatment, Vol. 51(3), pp. 1073–1085, IF: 3.363.
* (2017) "What Clinical Information Is Valuable to Doctors Using Mobile Electronic Medical Records and When?" Journal of Medical Internet Research, IF: 5.18.
(2017) "Usage Pattern Differences and Similarities of Mobile Electronic Medical Records Among Health Care Providers," JMIR Mhealth and Uhealth, IF: 4.64.
* (2016) "Depression Screening Using Daily Mental-Health Ratings from a Smartphone Application for Breast Cancer Patients," Journal of Medical Internet Research, IF: 4.53.
Conference Proceedings
* # (2023) "SurProGenes: Survival Risk-Ordered Representation of Cancer Patients and Genes for the Identification of Prognostic Genes," Proceedings of the 40th International Conference on Machine Learning.
(2022) "Deep Learning Models with Stratification-based Loss Function on Domain Knowledge-based Time series Data: Hypotension Prediction," 2022 IEEE International Conference on Big Data (Big Data).
(2016) "Dynamics of Social Influence on New Employees’ Use of Volitional IS: m-EHR Case in Hospital Setting," International Conference on Information Systems (Conference Proceeding).
(2016) "Are Uber Really to Blame for Sexual Assault? Evidence from New York City," International Conference on Electronic Commerce (Conference Proceeding).
* (2015) "Detecting Depression of Cancer Patients with Daily Mental Health Logs from Mobile Applications," International Conference on Information Systems (Conference Proceeding).
