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| Position | Lecturer (Assistant Professor) in Statistical Science |
| Phone (external) | 听 |
| Phone (internal) | 听 |
| Email (@ucl.ac.uk) | alessandro.barp |
| Personal webpage | |
| Themes | Computational Statistics, General Theory and Methodology, Statistics in Biomedicine and Health Economics |
Biographical Details
My academic journey began with the MMathPhys course at the University of Warwick, and then Part III of the Mathematical Tripos at the University of Cambridge (Downing College). I then pursued a PhD in mathematics at Imperial College London. Afterward, I undertook postdoctoral research at Cambridge and the Alan Turing Institute, focusing on geometric machine learning, before joining 果冻影院 as a Lecturer in Statistical Science.听
Research Interests
I co-lead the research group. My research is centred on leveraging the geometric structure of the data and models to develop computationally tractable methodologies with theoretical guarantees. Key areas of interest include numerical integration, model inference, and interpretable deep learning for biomedical applications.
Selected publications
- Barp, A., Briol, F. X., Duncan, A., Girolami, M., & Mackey, L. (2019). Minimum Stein discrepancy estimators.听Advances in Neural Information Processing Systems,听32.
- Simon-Gabriel, C. J., Barp, A., Sch枚lkopf, B., & Mackey, L. (2023). Metrizing weak convergence with maximum mean discrepancies.听Journal of Machine Learning Research,听24(184), 1-20.
- Barp, A., Simon-Gabriel, C. J., Girolami, M., & Mackey, L. (2024). Targeted separation and convergence with kernel discrepancies. In听NeurIPS 2022 Workshop on Score-Based Methods. To appear in the Journal of Machine Learning Research.