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| Position | Lecturer (Assistant Professor) in Statistical Science |
| Phone (external) | |
| Phone (internal) | |
| Email (@ucl.ac.uk) | alessandro.barp |
| Personal webpage | https://alebarp.github.io/ |
| Themes | Computational Statistics, General Theory and Methodology, Biostatistics |
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 University College London as a Lecturer in Statistical Science.
Research Interests
I co-lead the Fundamentals of Statistical Machine Learning 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. Advances in Neural Information Processing Systems, 32.
- Simon-Gabriel, C. J., Barp, A., Schölkopf, B., & Mackey, L. (2023). Metrizing weak convergence with maximum mean discrepancies. Journal of Machine Learning Research, 24(184), 1-20.
- Barp, A., Simon-Gabriel, C. J., Girolami, M., & Mackey, L. (2024). Targeted separation and convergence with kernel discrepancies. In NeurIPS 2022 Workshop on Score-Based Methods. To appear in the Journal of Machine Learning Research.