I am a Lecturer at the Centre for Intelligent Infrastructure within the Department of Civil and Environmental Engineering. I am interested in computing with imprecision. A number can be imprecisely specified as an interval, a pair of moments, a probability distribution, a set of distributions, and many more. With imprecise computation we can automatically propagate the uncertainty for model verification and make rigorous inference with scarce empirical data for model calibration and validation. These methods are very useful to build trust in simulation for structural reliability and structural-health monitoring. 

I obtained a PhD in risk and uncertainty in 2015 from the University of Liverpool’s Institute for Risk and Uncertainty. I hold a Bachelor and Master of Engineering both cum laude in civil and environmental engineering from the University of Rome, Roma Tre. After the PhD, I was research associate and academic manager at the Centre for Doctoral Training in Risk and Uncertainty of the University of Liverpool for over two years. In 2018 I was appointed research associate for the EPSRC-UKRI Programme Grant on digital twins for improved dynamic design.

* Humane algorithms for quantitative science 

* 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   

* Structural calculations with rigorous uncertainty propagation  

* Valid and rigorous machine learning inference 

* Verification with computer arithmetics (intervals, units, automatic differentiation, probabilistic arithmetic, etc.)

Structural engineering theory

Computer programming

Interval computation

Probability theory

Machine learning

I currently teach third year students about statically indeterminate structures, and fourth year students about the 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.

* Mathematics for engineering
* Computing with imprecision 
* Verification and validation
* Uncertianty propagation 
* Statistical inference
* Structural analysis 
* Reliability analysis
* Deep learning
* Programming 
* Optimisation