https://www.nsf.gov/awardsearch/show-award?AWD_ID=2533946

This project develops new mathematical methods and computational algorithms to enable safe, reliable deployment of digital twins in healthcare, with a focus on managing Type 1 diabetes through personalized insulin delivery. It tackles three critical barriers that limit the trustworthiness of deep learning-based digital twins for healthcare, including identifying reliable models capable of accurately representing individualized glucose-insulin dynamics, quantifying predictive uncertainty under data scarcity, patient variability, and sensor errors, and validating treatment recommendations made by deep learning models under physiological fluctuations and potential control system faults. To close these gaps, the research advances three integrated technical objectives. First, it introduces an iterative Bayesian model selection and validation strategy for discovering deep learning models with accurate and reliable predictions, using population-level clinical data. Second, it implements algorithms and scalable cyberinfrastructure for real-time adaptation of the digital twin to individual physiology, including risk-averse insulin control. Third, it establishes rigorous methodologies for validating treatment recommendations by the deep learning-based digital twin, using both in silico simulations and clinical datasets. Intellectual contributions include a mathematical and computational framework for decision-making under uncertainty in physiological modeling, derivation of a posteriori error bounds for deep learning forecasts, and scalable techniques for optimal control under high-dimensional uncertainty. The resulting methods provide a generalizable blueprint for constructing, evaluating, and de-risking digital twins in a wide range of biomedical applications beyond diabetes care.

Namit Juneja (RA), Varun Chandola (co-PI)

Prof. Danial Faghihi (PI), Prof. Bruce Pitman, Prof. Tarun Raj Singh, Dr. Nikolay Simakov, Dr. Lucy Mastandrea

National Science Foundation

07/2025 - 06/2028