I am a proponent of the use of (semi-)formal process models to manage data-driven collaboration in a transparent, trustworthy way. Such models can form workflow contracts that are both understandable by humans and executed and mediated by machines. In this context, logical reasoning is key to improving safety by verifying correctness (both at design and at runtime) and to establish trust in collaborative environments. As such, safer and more responsible, AI-enabled systems have been at the core of my research.
This includes research topics such as process modelling, formal verification, workflow analysis and enactment, complex IoT event analysis, process innovation, and applications such as health informatics, as described below.
I am also interested in various related topics, such as decision support, simulation and optimisation, event-based and distributed systems, operations research, data governance, and social machines.
I work on the systematic modelling of processes, using diagrammatic models with links to process algebras (π-calculus, session types, Petri-Nets), as well as using process mining techniques.
Process & Data
Workflow Analysis & Enactment
A big part of my research is AI-enabled analysis and deployment of coordination protocols as workflows, both in simulation and live. This includes concurrent enactment, decentralised architectures, and discrete event simulation.
Process & Data Innovation
I investigate cultural, ethical, and resource constraints in data-driven innovation and change management in large and public organisations, including issues of privacy, responsibility, and data governance.