Lecturer in Predictive Analytics and Director of the FinTech PhD Programme
Roles and Responsibilities
- Director of the Domain-Driven Machine Learning Lab
- Director of the PhD Programme in Financial Technology
- Advisory Board Chair of the Centre for Data, Culture and Society
Background
Ben is a Lecturer in Predictive Analytics and joined the Business School from a previous McWilliams Fellowship at Carnegie Mellon University and the Pittsburgh Supercomputing Center in the United States. Having spent time as a Visiting Researcher at the Max Planck Institute for Astrophysics in Germany, Ben served as a Review Panelist for the NASA Science Mission Directorate, the US Department of Energy's Office of Science Graduate Student Research, and the UK Research and Innovation Funding Service, and worked in the UK's investment management sector as a Research Engineer for Machine Learning.
Additional memberships include the Centre for Statistics at the School of Mathematics, the Centre for Financial Innovations at the Edinburgh Futures Institute and the Scottish Centre for Crime and Justice Research. Ben holds a PhD in Astrophysics and an MSc in Artificial Intelligence from the University of Edinburgh, as well as accreditations as a Professional Statistician from the American Statistical Association and a Fellow of the Higher Education Academy.
Research Interests
Ben’s research is centred on artificial intelligence and addresses domain challenges using machine learning, statistical inference and high-performance computing. With a strong focus on interdisciplinary collaborations and technology transfer between fields, this covers both impactful applications and the problem-driven development of new methods. Current research projects include generative modelling for market microstructure in high-frequency trading, privacy-preserving graphical and deep learning techniques in central bank data disclosure, and geospatial analysis for public safety and health impacts.
Primary areas for PhD supervisions are listed below, but prospective students are welcome to reach out with their CV and a research proposal for other project ideas that broadly align. This includes machine learning in other fields, provided a second supervisor acts as a domain expert for the respective area of application, students with a background in other numerate disciplines and joint supervisions with other schools at the university.
- Domain-specific machine learning methods
- Financial technology and econometric analysis
- Spatio-temporal statistics for societal challenges
- Deep learning frameworks and synthetic datasets