I work in the broad area of Computational Intelligence (CI) for Cyber-physical System (CPS), by developing tools and methods of Artificial Intelligence (AI), Machine Learning (ML), Quantum Mechanics (QM), Performance Based Engineering (PBE), Uncertainty Quantification (UQ) and Risk Analysis, Decision Support Tool (DST) for academia and industry for more than 20 years. 

My current research interests focus on the development of new Quantum Physics AI (QPAI) aimed at sustainable and resilient urban communities; more specifically the Quantum Physics Digital Twin (QPDT) whose digital model is going to be deployed within the opensource software QSTAR (Q*)

My teaching is about AI, statistics and Machine Learning, Uncertainty Quantification and Risk Analysis for Sustainable and Resilient design.  

Messina, Reggio Calabria: resilient design under imprecise probability, Risk Digital Twin (RDT) under imprecise data

National University of Singapore: Stochastic Dynamic Analysis, RDT for offshore systems

Politecnico Milano: UQ, Quantum UQ (QUQ), Quantum AI (QAI)

University of California at Berkeley: Decision Support Tool (DST) under uncertainty, AI and UQ for sustainable and resilient design, RDT, Quantum UQ and AI

UNSW Sidney: RDT,  low-carbon building design

SMART, MIT: UQ and DST for resilient railway system

Participation in national and international committees, panels and steering groups about digitalisation for teaching, sustainability and resilience of urban communities.