Andrei Leonard Nicusan is a PhD student at the University of Birmingham focusing on data-driven engineering across scales. He published featured articles and Scientific Highlights on machine learning-based positron emission particle tracking algorithms. His work on evolutionary algorithms for simulation calibration, optimisation and physics discovery in granular mechanics has raised more than £260,000 from research and industrial funding bodies. His frameworks are actively being used in projects with companies such as JM, GranuTools, JDE, FMC, Recycling Technologies.
Leonard is working on an automatic equation-discovery tool forming a complete data-driven solution for complex particulate systems at all scales. Rather than finding an equation’s coefficients that fit a given dataset, it finds the equation itself - and so may discover underlying laws in fundamental granular mechanics or find accurate correlations for industrial use. The Turing Institute’s vast expertise in the theoretical underpinnings of combinatorial optimisation is vital in the technique’s development and its successful application to real open problems in collaboration with its Fellows.