교수소개
이민지 교수는 뇌파, 심전도, 기능성 자기공명영상 등 생체신호와 신경영상을 활용하여 의료 인공지능, 디지털 헬스케어, 뇌-컴퓨터 인터페이스 등을 위한 머신러닝/딥러닝 모델 개발을 주로 연구하고 있다. 2021년 고려대학교에서 생리적, 약리적, 병리적 조건에서의 설명 가능한 딥러닝 기반 의식 분리 연구로 박사학위를 받았으며, 이후 SK하이닉스에서 Data Scientist로 근무하였다. 2023년부터 가톨릭대학교에서 계산신경지능 연구실을 운영해 오고 있으며, 미국, 독일, 벨기에 등과 활발한 국제공동연구를 수행하고 있다.
Professor Minji Lee is mainly researching the development of machine learning/deep learning models for medical artificial intelligence, digital healthcare, and brain-computer interfaces using biosignals and neuroimaging such as electroencephalogram, electrocardiogram, and functional magnetic resonance imaging. She received her doctorate in 2021 from Korea University for disentangling consciousness with explainable deep learning, and has been running a Computational NeuroIntelligence Lab. at The Catholic University of Korea since 2023. She is conducting active international collaborative research with the United States, Germany, and Belgium.
최종학력
2021.02.25 | 고려대학교 | 뇌공학과 | 공학박사
연구실적
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2024.12
| 제1저자
| CURRENT OPINION IN NEUROLOGY, 제37권 6호, pp.614-620
Artificial intelligence and machine learning in disorders of consciousness -
2024.10
| 제1저자
| APPLIED SCIENCES-BASEL, 제14권 21호, pp.1-15
Neuromodulation Effect According to Lesion Location After Dual-Mode Brain Stimulation in Patients with Subacute Stroke: A Preliminary Study -
2024.09
| 교신저자
| JOURNAL OF MEDICAL INTERNET RESEARCH, 제26권 1호, pp.1-22
Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study -
2024.07
| 공동저자
| IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 제32권 1호, pp.2793-2804
Iteratively Calibratable Network for Reliable EEG-Based Robotic Arm Control -
2024.06
| 제1저자
| IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 제32권 1호, pp.2533-2544
Automatic Sleep Stage Classification Using Nasal Pressure Decoding Based on a Multi-Kernel Convolutional BiLSTM Network -
2024.04
| 제1저자
| IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 제32권, pp.1767-1778
Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients -
2023.12
| 교신저자
| JOURNAL OF MEDICAL INTERNET RESEARCH, 제25권
Explainable Artificial Intelligence Warning Model Using an Ensemble Approach for In-Hospital Cardiac Arrest Prediction: Retrospective Cohort Study -
2023.08
| 제1저자
| FRONTIERS IN PHYSIOLOGY, 제14권
SeriesSleepNet: an EEG time series model with partial data augmentation for automatic sleep stage scoring -
2022.02
| 제1저자
| NATURE COMMUNICATIONS, 제13권 1호, pp.1-14
Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning