Bio Computing & Machine Learning Lab
At BCML, we explore the intersection of biology, computation, and machine learning to push computational neuroscience forward.
Our research focuses on Spiking Neural Networks, Reinforcement Learning, and Medical AI to advance healthcare innovation.
Department of Computer Engineering, Kwangwoon University, Seoul, South Korea
Research Projects
Explore our cutting-edge research
Physics-based bed simulation that generates synthetic fall data and transfers fall-risk prediction to real-world video.
An end-to-end AI workflow that captures nailfold capillaroscopy (NFC) images, enhances capillary visibility, and quantifies biomarkers for autoimmune and vascular disease screening.
Developing energy-efficient robotic control systems using SNNs with R-STDP and TD3 reinforcement learning
Developing energy-efficient robotic control systems using SNNs with R-STDP and TD3 reinforcement learning
HAR using multiple sensors including RGB, depth, IMU, and event-based sensors with advanced feature fusion
Latest Publications
(2026).
Novel approach to blood pressure estimation using graph-based signal relationships.
Biomedical Signal Processing and Control, 125.
(2026).
근전도 신호 기반 궤적 예측을 통한 디지털 입력 시스템 및 그 방법.
한국 특허 출원 10-2025-0166182 (2025.11.06) / 등록 10-3027736 (2026.09.29).
(2026).
Graph-enhanced bidirectional GRU for anomaly detection in power generation environments.
Data Mining and Knowledge Discovery, Vol.40, No.6, 102.
(2026).
GARS: Gap-Aware Residual Selection for Long-Horizon Time-Series Forecasting.
MDPI Electronics, Vol.15, No.18, 4137.
(2026).
비접촉 방식으로 생체신호를 추정하는 장치.
한국 특허 출원 10-2022-0183613 (2022.12.23) / 등록 10-3001026 (2026.08.03).