Research & expertise
I combine scientific research with software development to study complex systems and support decisions that need to work beyond the lab.
Scientific research
The common thread is intelligent optimization: choosing, designing, and evaluating methods that fit the problem rather than applying a fixed recipe.
Search methods for problems where the solution space is large, uncertain, or shaped by competing objectives.
My work covers evolutionary algorithms, metaheuristics, hyper-heuristics, Bayesian optimization, reinforcement learning, and adaptive operator selection, with applications in routing, scheduling, image processing, and engineering.
Models designed not only to predict, but to support decisions that can be examined and improved.
I work with predictive modeling, explainable AI, deep learning, image analysis, and data preparation in transport and biomedical applications.
Planning transport services around changing demand, disruptions, safety, and day-to-day constraints.
Current applications include dynamic fleet routing, demand-responsive transport, parcel logistics, roadworks planning, traffic simulation, transport modeling, and geospatial safety analysis.
Building the software needed to run experiments, compare alternatives, and share results with project teams.
I develop optimization pipelines, simulation workflows, experimental prototypes, and scenario evaluations for multidisciplinary R&D projects.
Methods, software & technical capabilities
I use research software to test assumptions, compare alternatives, and communicate evidence with collaborators and domain specialists.
My work typically combines algorithmic modelling, experiment design, data preparation, and implementation. The technical stack follows the research question and the constraints of the application.
I prioritise transparent assumptions, reproducible workflows, and outputs that can be discussed by both technical and domain experts.