I am assistant professor in the Department of Mathematics and Computer Science at the Eindhoven University of Technology, where I am part of the Stochastics section. I participate in the Networks Gravitation (NWO-Zwaartekracht) project, an NWO funded initiative to build self-organizing and intelligent networks by using algorithms and stochastics. I am also part of the DeSIRE research program on resilient engineering funded by the 4TU-call âHigh Tech for a Sustainable Futureâ and I am in the think-tank of the newly established 4TU Centre for Resilience Engineering[AHv1]. Furthermore, I am a co-applicant in the PrimaVera (NWA-ORC) project and the Real-time data-driven maintenance logistics (NWO â âBig Data: real time ICT for logisticsâ) project.
Nowadays, we rely continuously and indispensably on our systems: In todayâs society, we expect systems to be operating at all times. In todayâs industry, we expect to have fully automated systems, that do not require constant human intervention. We are already designing smart systems that ideally report on their condition and, in the future, we envision that these systems will autonomously identify imminent failures, infer their causes and resolve the problem. As such, one key objective is to minimise failures by trying to accurately predict them before they happen. In several cases this can be achieved, however failures cannot be completely avoided. In case a failure occurs, it is paramount to ensure that the system can withstand a major disruption within acceptable degradation margins and that it can recover within an acceptable time, viz that the system is resilient.
In my work, in an effort to predict failures, using data and the domain knowledge, I model the failure mechanisms of systems. Such models are used for logistics purposes, and in particular to investigate the optimal course of action (maintenance) to avoid imminent failures, whilst minimising the costs and whilst ensuring the highest level of resilience. To this end, I use mathematical models and smart algorithms stemming from stochastics, artificial intelligence (AI) and machine learning (ML). My work sets the stepping stones in building smart resilient autonomous systems, by revealing the value of data for high-quality decisions.
As a member of the 4TU Centre for Resilience Engineering think tank, I would like to create a holistic approach (in the form of a toolkit) for the modelling of failure mechanisms, the accurate prediction of imminent failures, the performance analysis of the systemâs resilience, and the optimal maintenance planning for complex sociotechnical systems. Such a holistic approach cannot simply utilise existing methods which at an earlier stage might have been sufficient. In order to overcome the hurdles, we must revolutionise our thinking, revolutionise our methods and become beacons of knowledge to the younger generations. The centre and its fellows provide the necessary ingredients needed for this groundbreaking research.