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Learning robust temporal representations for dynamic patient trajectories and time series data analysis
Integrating medical knowledge with multimodal AI and simulating organ-specific medical digital twins
Graph-based embedding of missing information and learning balanced representation for causal relationship modeling
Predictive soft clustering for time series using variational inference of GMM and continuous phenotyping
Supervised contrastive learning and deep representation learning for electronic health records
Agentic LLM for clinical question answering and PEFT-based test-time adaptation of language models
Robust representation learning from incomplete multimodal clinical data with cross-modal temporal alignment
Safe and clinician-informed reinforcement learning for medical decision making and treatment optimization
Sequential anomaly detection for clinical deterioration and early warning systems in ICU settings
Medical Intelligent Digital Twin
Causality-aware Embedding of Irregular Time Series
Temporal Predictive Soft Clustering
Contrastive Learning for EHR
Agentic LLM for Clinical Question Answering in the ICU
PEFT-based Test-time Adaptation of LLMs for EHRs
LLM-based Representation Learning for Irregular Time Series
Self-supervised Foundation Model for ICU
Multimodal Fusion of EHR
Wearable IoT-enabled Health Monitoring System
Frictional Off-policy RL
Safe and Reasonable Medical AI
Medical Dead-ends for Safe RL
Multimodal RL for Embedding Networks
Sequential Anomaly Detection for Clinical Deterioration
출처: 연구실 홈페이지
현재 재학생
11명
최근 5년 졸업
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