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Publications

Chen Yu, Rocchetta Roberto, Nespoli Lorenzo, Medici Vasco, , ,

Proceedings of the 36th European Safety and Reliability Conference (ESREL 2026) European Safety and Reliability Conference, pp. 2949-2956 (2026)

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Proceedings of the 36th European Safety and Reliability Conference European Safety and Reliability Conference, pp. 2426-2433 (2026)

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Proceedings of the 36th European Safety and Reliability Conference European Safety and Reliability Conference (2026)

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Proceedings of the 36th European Safety and Reliability Conference European Safety and Reliability Conference (2026)

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Proceedings of the 36th European Safety and Reliability Conference European Safety and Reliability Conference, pp. 899-906 (2026)

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International Journal of Disaster Risk Reduction Vol 135 (2026)

Teaching

I currently teach third year students about statically indeterminate structures, and fourth year students about the听dynamics of single and multi-degree-of-freedom systems. I have been and am currently invovled in developing lecture material and in shaping the structural analysis curriculum.

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Research Interests

I am primarily interested in applying validated numerics to engineering problems for the automatic and rigorous verification of engineering calculations. My interest is in formal methods that can prove the mathematical correctness of computational solvers in the presence of input uncertainty. These methods can be paired with language models to accelerate engineering decision-making. How exactly these language models can be engineered by researchers to yield provably correct responses for any given problem is of great interest. I am also interested in the democratization of artificial intelligence for research and scientific computing and in the development of human-centred algorithms that translate complex computational workflows into transparent, auditable, accessible and actionable digital tools.

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* Humane algorithms for quantitative science 听
* Reproducibility and open computational science
* Uncertainty quantification for green-house gas calculations
* Open-source digital twins for structural-health monitoring
* Automated compliance checking for engineering calculations 听
* Structural calculations with rigorous uncertainty propagation听
* Valid and rigorous machine learning inference
* Verification with computer arithmetics (intervals, units, automatic differentiation, probabilistic arithmetic, etc.)

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Professional Activities

Member

31/10/2026

Advisor

15/6/2026

Participant

15/6/2026

Peer reviewer

5/2026

Peer reviewer

1/2026

Speaker

4/12/2025

Projects

Paez Jimenez, Mariana Gabriela (Post Grad Student) De Angelis, Marco (Principal Investigator) Giesekam, Jannik (Co-investigator) Shipton, Zoe (Co-investigator)

The uncertainty inherent in CO2 estimations at the organisational and product levels has received significantly less academic scrutiny than national or global inventories, yet such CO2 estimations serve as foundational data for higher-level carbon accounting. Carbon accounting often relies on practitioners whose expertise is not climate science, leading to high levels of epistemic uncertainty, methodological variation and data variability. This project addresses three critical questions: (1) How much uncertainty is inherent in these CO2 estimations? (2) Where does the uncertainty come from and how can it be mitigated? (3) How can the uncertainty be communicated to enable decisions towards net zero targets?

01-Jan-2025 - 30-Jan-2028

Tubaldi, Enrico (Principal Investigator) De Angelis, Marco (Co-investigator) Moghaddasi Kelishomi, Hamed (Co-investigator) Patelli, Edoardo (Co-investigator) Sentenac, Phillippe (Co-investigator) Tarantino, Alessandro (Co-investigator)

01-Jan-2025 - 31-Jan-2028

Tubaldi, Enrico (Principal Investigator) De Angelis, Marco (Co-investigator) Pytharouli, Stella (Co-investigator) Tarantino, Alessandro (Co-investigator)

01-Jan-2025 - 31-Jan-2027

De Angelis, Marco (Principal Investigator) Ochnio, Dawid (Post Grad Student) McIntosh, Angus (Post Grad Student) Loayza Romero, Karen Estefania (Co-investigator) Kazashi, Yoshihito (Academic) Ruggeri, Michele (Academic) Bi, Sifeng (Academic)

StrathDRUMS aims to train the next generation of interdisciplinary uncertainty quantification specialists who can design and analyse state-of-the-art computational techniques and apply them to real-world challenges. An important aspect of this CDT is the non-deterministic and data-driven angle to modelling, which is crucial to overcome the limitations of classical deterministic approaches. StrathDRUMS graduates will develop rigorous uncertainty quantification skills including characterisation, propagation, verification and validation. Data-driven uncertainty-aware model responses allow for a more comprehensive understanding of the system being studied, improving the reliability of the predictions and enabling the rigorous verification of numerical simulations.

01-Jan-2023 - 31-Jan-2029

McCann, Matthew (Post Grad Student) De Angelis, Marco (Principal Investigator)

Major global emission inventories such as EDGAR, national GHG inventories, and the IPCC tiered approach provide critical baseline data for emissions monitoring. Despite some strengths, existing emission inventories are limited by static estimates that can very quickly become outdated and by over reliance on default emission factors, which may under or overestimate actual emission within changing industrial contexts. Many of these inventories lack robust uncertainty quantification methods such as Monte Carlo simulation or error propagation. This project investigates the use of rigorous probabilistic and statistical modelling tools towards a framework for both aleatory and epistemic uncertainty quantification of CO2 estimation. State-of-the-art machine learning offers new exciting possibilities for the modelling, estimation and forecasting of CO2 emissions, providing tools that can handle complex, non-linear relationships within large datasets.

01-Jan-2023 - 31-Jan-2029