Generative AI for Structure Discovery in Cardiovascular Digital Twins
Mentors: Dr. Marco Tezzele and Dr. Jimena Martin Tempestti
Overview
A digital twin (DT) is a virtual representation of a physical object that dynamically evolves with its real-world counterpart through sensed data, and provides value through optimal decision-making. Effective DTs rely on accurate mathematical representations of causal dependencies and temporal evolution, often modeled via Probabilistic Graphical Models (PGMs). Traditional approaches frequently assume a static, predefined graph topology (e.g., first-order Markov chains) or rely on simplified expert heuristics, which may fail to capture the rich, long-range dependencies inherent in physiological systems.
This project investigates how generative AI can automate the discovery of optimal mathematical structures for DTs. Students will develop a framework where a DT is represented as a dynamic PGM. Starting from a fully connected graph encompassing observations, latent digital states, quantities of interest, and actions, students will employ generative methods to prune and optimize the graph topology.
Research Challenges
The research will specifically focus on two “New Mathematics” challenges:
- Topology Learning: Analyzing how the graph structure affects the identifiability and estimation of digital states
- Temporal Memory: Investigating whether higher-order Markov chains generated by AI models yield better predictions than standard memoryless models
Validation
The methods will be validated on simulated cardiovascular datasets, aiming to predict patient outcomes and the optimal treatment plan for cardiovascular diseases.