General Research of End-to-end Edge-intelligent Networks and Security (GREENS) Laboratory
Our vision of GREENS laboratory is to create trustworthy, intelligent, and interconnected healthcare ecosystems—where AI understands, IoT senses, and Blockchain protects. Together, these technologies redefine how health data is analyzed, shared, and trusted in the digital age.
We develop Large Language Model (LLM)–driven healthcare systems that transform how medical knowledge, patient data, and human reasoning interact. Our research explores how advanced multimodal LLMs can understand and integrate electronic health records (EHRs), biomedical texts, wearable sensor data, and clinical dialogues to support diagnosis, personalized treatment, and patient engagement.
Key directions include:
Medical reasoning and clinical dialogue systems — conversational AI for mental health support, symptom explanation, and clinical decision assistance.
Multimodal fusion — combining textual, physiological, and genomic data for context-aware diagnostic modeling.
Ethical and trustworthy LLMs — integrating privacy, interpretability, and bias mitigation for safe deployment in medical environments.
By uniting semantic reasoning with domain expertise, our work aims to build the next generation of intelligent clinical companions—AI systems that understand medical language as deeply as clinicians do.
Our AIoT (Artificial Intelligence of Things) research focuses on bringing intelligence closer to the body and environment through TinyML-enabled wearables and edge computing. We design energy-efficient, privacy-preserving, and adaptive sensing networks that can analyze biosignals and behaviors in real time—without relying solely on the cloud.
Key directions include:
TinyML for physiological monitoring — on-device models for ECG, respiration, motion, and stress detection.
Edge-cloud hybrid architectures — integrating local analytics with cloud-based LLM reasoning for continuous care.
Adaptive routing and sensor collaboration — intelligent data pathways that reduce latency, conserve energy, and prioritize urgent medical events.
Our goal is to create autonomous, always-on healthcare ecosystems—smart environments that sense, learn, and respond instantly to individual health changes while safeguarding data privacy.
We investigate how blockchain and distributed ledger technologies can ensure trust, traceability, and accountabilityin healthcare data systems. In an era of AI-driven analytics and federated data exchange, protecting patient identity and data integrity is paramount.
Key directions include:
Blockchain-secured data provenance — verifying the origin and authenticity of health data from IoT devices and clinical systems.
Privacy-preserving federated learning — enabling collaborative AI training across hospitals without sharing raw data.
Smart-contract-based consent and access control — empowering patients to manage who uses their data and for what purpose.
Our vision is to build a trustworthy digital health infrastructure where data ownership remains with individuals, yet healthcare innovation flourishes through secure, verifiable, and transparent collaboration.
GREENS Laboratory is working on the following research topics:
Cyber Security - TAXI server:
Artificial Intelligence
Infrastructure
Blockchain technology for Privacy and Cybersecurity
Medical Data Management