I am delighted to announce that I have been awarded one of the two ECCOMAS awards for the best PhD theses 2026. The award was announced on July 20, 2026, during the 17th World Congress on Computational Mechanics and 10th European Congress on Computational Methods in Applied Sciences and Engineering.

The evaluation committee selected my thesis "Mathematical Models and Numerical Methods for Neurodegenerative Diseases" with the following motivation: 

"These theses are outstanding works in Computational Methods in Applied Sciences, combining excellent knowledge of both theory and practice, with special insight into the fundamental and computational aspects.

The two works advance the frontiers of ECCOMAS and open our community to new problems and methodologies. The award decision was based on the relevance of the topics, the originality of the theses, their scientific content, the innovative numerical developments, and the personal involvement of the candidates. Both theses are very well written, well-illustrated, and are accompanied by a significant scientific production.

The first thesis focuses on the mathematical modelling and numerical simulation of neurodegenerative diseases. It develops and analyses high-order discontinuous Galerkin methods on polygonal/polyhedral grids, proving stability estimates and a priori convergence results for several models, including brain poromechanics. Additional contributions include an innovative mesh agglomeration technique based on geometric machine learning and addressing patient-specific parameter calibration through inverse Uncertainty Quantification methods coupled with reduced-order solvers."

Check out the latest article from A. Ahern, A. Goriely, E. Kuhl, P.F. Antonietti, and me, "A whole-brain model of amyloid beta accumulation and cerebral hypoperfusion in Alzheimer's disease" recently published in Computer Methods in Applied Mechanics and Engineering. 

In this work, we propose a whole-brain model coupling amyloid beta and blood vessel through a hybrid model consisting of a reaction-diffusion system for the protein dynamics and porous-medium model of blood flow within and between vascular networks. 

Motivated by the "two-hit vascular hypothesis" of Alzheimer's disease that hypoperfusive vascular damage triggers amyloid beta pathology, we also demonstrate that localized hypoperfusion, in response to injury, can destabilize the healthy steady state and trigger brain-wide disease outbreak.

I am delighted to announce that I have been awarded, ex aequo, the Anile-ECMI Prize for Mathematics in Industry 2026. The award was announced on June 29, 2026, during the 23rd ECMI Conference on Industrial and Applied Mathematics.

The prize is  funded by ECMI and the Fraunhofer Institute for Industrial Mathematics (ITWM, Kaiserslautern). The evaluation committee selected my thesis "Mathematical Models and Numerical Methods for Neurodegenerative Diseases" with the following motivation: 

"For his work on the development of digital twins of the human brain and neurodegenerative diseases. His research combines rigorous mathematical foundations with advanced modeling and numerical methods, representing a significant step toward understanding both physiological and pathological brain functions. The work is of high scientific value and has considerable potential impact in areas such as neurology."

As part of the award, I had the possibilty to present my scientific results in a plenary session at the ECMI Conference. 

Our last preprint "The lymph 2.0 library: p-adaptive algorithms and parallel assembly strategies for polytopal DG methods" from C.B. Leimer Saglio, S. Pagani, P.F. Antonietti, and me is now out on ArXiv and MOX Report. 

This work presents a new release of the lymph 2.0 library, an open-source MATLAB framework for high-order discontinuous Galerkin discretizations on general polytopal meshes. This version is extended to support discretizations with element-wise polynomial approximation degrees, which allows the design of p-adaptive strategies based on a posteriori error indicators. In addition, the library introduces a unified assembly framework that abstracts the construction of discrete operators from the underlying physical model, improving code modularity, parallelism, maintainability, and extensibility. Moreover, the proposed approach enables shared-memory parallelism through dedicated parallel tools. Several numerical examples demonstrate the effectiveness of the proposed developments in reducing the computational cost while preserving approximation accuracy.

Polygonal and Structure-Preserving Methods for PDEs Discretization

Mathematical Modeling of Neurodegenerative Diseases