About team RL
● Mission:
We develop personalized AI diagnostics using reinforcement learning to adapt to individual brain connectivity and medical signals
● Scope:
We use reinforcement learning to reflect individual brain differences and reduce site-specific noise in multi-site fMRI, enabling robust and generalizable diagnostics
● Goal:
Our goal is to build a clinically scalable AI diagnostic system that adapts to patient-specific variability in medical pattern, and balances multiple diagnostic goals for individualized care
Available internship topics
● Conditional Diffusion Modeling for Personalized Medical Signal Synthesis and Reward-Based Quality Evaluation
● Simulation and evaluation of multi-agent reinforcement learning models for role-based diagnostic task distribution
Our research topics
Personalized Decision Making with Reinforcement Learning
● Our team is using reinforcement learning to support optimal decision-making in personalized and complex medical situations. By continuously learning from patient-specific data such as ROI and EEG-based traits, we generate insights that contribute to more efficient care approaches and improved patient outcomes.
Related publications
[Under review] Personalized Brain Dynamic Functional Connectivity with Hierarchical Reinforcement Learning
[Under review] Ensemble Reinforcement Learning for Personalized Mild Cognitive Impairment Diagnosis
[JBHI'24] Sparse Graph Representation Learning based on Reinforcement Learning for Personalized Mild Cognitive Impairment (MCI) Diagnosis
Feature Selection with Multi-Agent Reinforcement Learning
● We are developing a multi-agent reinforcement learning framework that identifies the most relevant features in medical datasets. This approach enhances diagnostic accuracy and model efficiency by selecting critical biomarkers through agent collaboration.
Related publications
[IEEE Trans. SMC'24] MARS: Multi-Agent Reinforcement Learning for Spatial-Spectral and Temporal Feature Selection in EEG-based BCI