Michela Prunella is a First-Level Technologist and a Ph.D. Candidate in Autonomous Systems at the Polytechnic of Bari. She received B.Sc. and M.Sc. degrees (both cum laude) in Medical Systems Engineering from the Polytechnic of Bari in 2020 and 2022. Her master’s thesis focused on developing an automatic 3D segmentation algorithm for patient-specific renal vasculature and parenchyma from CTA scans. She subsequently worked as a research fellow in the “Cognitive Diagnostics” Public–Private Laboratory with Comau S.p.A., where she developed deep learning–based defect detection systems, feature engineering strategies, and computer vision algorithms. 

Since November 2025, she has been involved within the PNRR Complementary D34H project.

Her Ph.D. research titled “Computational Pathology and Mechanistic Modeling for Medical Digital Twins: Enabling Disease Trajectory Prediction and Therapy Optimization”, combines data-driven learning frameworks with mechanistic, biologically informed models to predict disease trajectories and proactively optimize therapeutic strategies. She develops pathomic pipelines to extract interpretable prognostic and predictive biomarkers from histopathological images, and pharmacometric frameworks capable of simulating multiscale disease dynamics at the patient level. Her work includes model-based therapy optimization, linking neural network predictions with control and optimization methods for adaptive treatment selection, and Digital Twin development for oncology, nephropathology, and neonatal sepsis. 

In recognition of her contributions to computational modeling, she was awarded Runner-Up in the International Modelling Challenge “Digital Twin Builder for Health Incubator,” applied to the unmet clinical need “Building the Next Generation Physiologically-based Framework for Precision Dosing of Renally Cleared Medicines in Neonates” organized by Katholic University of Leuven, Belgium.