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WELCOME TO THE MATH-DIGITAL-TWIN PROJECT



This project aims to develop the mathematical foundations for a digital twin
(DT) system for individuals with autism spectrum disorder (ASD), focusing on
dynamic modeling, prediction, uncertainty quantification, and treatment or
intervention recommendation through DT-based optimization. This project is
supported by NSF award at the George Washington University (NSF DMS-2436216) and
George Mason University (NSF DMS-2436217).

The specific goals of this project include:

 * Develop computational models based on conditional variational auto-encoders
   (CVAE) and longitudinal CVAE to analyze brain activities, and
   neurodevelopmental processes.
 * Create a novel bilevel formulation for fine-tuning foundational models to
   predict ASD outcomes.
 * Develop a model-free conformal prediction procedure to ensemble predictions,
   integrating various types of uncertainties.
 * Develop a DT-based reinforcement learning framework for personalized
   treatment plans to improve clinical outcomes.

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