Digital Twin Powered Decision Making AI

Reinforcement learning + online adaptation for personalized policies

Nov 1, 2024 1 min read Research

Digital Twin Powered Decision Making

Overview

Digital Twin Powered Decision Making AI combines reinforcement learning with online adaptation to create personalized policies for real-world applications.

Key Features

Digital Twin Technology: Virtual representations for simulation and optimization
Reinforcement Learning: Adaptive learning from interactions
Online Adaptation: Real-time policy updates based on feedback
Personalized Policies: Tailored decision support for individual needs

Applications

Smart Building: Optimization of energy and comfort
Supply Chain: Inventory and logistics management
Education: Personalized learning paths
Healthcare: Adaptive treatment planning

Deliverables

DHAI Lab Digital Twin Platform
Interactive Online Platform for Visualization of CT Scan Point Clouds

Research Team

This project is led by Dr. Lu Wang with contributions from Xinyu Qin and Ruiheng Yu.

Digital Twin Reinforcement Learning Adaptive AI

Lu Wang

Assistant Professor of Biomedical Engineering

My research interests are developing and applying Machine Learning, Data Mining and Statistical methods (e.g., Multi-task Learning, Survival Analysis, Clustering, Risk Factor Analysis and Causal Discovery) on various data including gene expression, electronic health/medical records, and DNA sequencing reads for both cognitive disorders (e.g., delirium, Alzheimer’s disease, dementia, major depressive disorder) and chronic diseases (e.g., cancer, obesity, hypertension). Inspired by the human factors approach, she also designs and develops Human-Centered Artificial Intelligence tools for users to integrate, visualize, analyze, and interpret health data in order to improve the interoperability and accessibility of AI-assisted healthcare decision support.