I am an expert engineer at DEME, working at the intersection of geotechnical engineering and data science. I completed my PhD at National University of Singapore in 2020 and my MSc and BSc at Ghent University. I spent the better part of the last decade in Asia and currently live in Belgium. I am also an avid cyclist and more recently a marathon runner.
Most of my work and research aims at improving predictive models by incorporating uncertainty an using real-world feedback data to calibrate and improve the modelling. Most problems I am trying to tackle are physics-based but with large uncertainties and poorly understood phenomena, prohibiting a closed-form description. I try to combine a deep understanding of the fundamental behaviour of my medium (especially soil) with the developments in machine learning, spatial variability modelling and Bayesian inference.
Working on better, data-informed, models is really only possible if proper data structures are in place with good ETL procedures, methods for reusable data processing and analytics and methods to expose the information. A lot of the data in civil an geotechnical engineering is messy, heterogenous and geospatial. Over the last few years, I spent a lot of time building a robust and flexible geospatial data platform for detailed geotechnical and operational data (geospatial datalake, GIS database, CI/CD pipelines, Python tools and web applications) for DEME.
I also like to play around with sensors and data logging to capture more feedback. For instance, I developed a wireless piezometer to monitor slurry fill during land reclamation.