I am currenlty a second year PhD student in Statistics supervised by Dr. Theodoros Damoulas and Prof. David Firth. I am part of a joint PhD program between the Department of Statistics at the University of Warwick and the Department of Statistics at the University of Oxford called the Oxford - Warwick Statistics Programme (OxWaSP). I am currently collaborating with Dr. Edwin V. Bonilla on Multi-task learning of inhomogeneous point processes. More information about my background can be found on my CV.
My research interests are at the interface of Bayesian statistics and computer science. I am interested in developing scalable machine learning algorithms for transfer learning and multi-task learning with Gaussian processes. I am also interested in linking probabilistic models and real world decision making problems with the aim of developing probabilistic frameworks for analysing and assessing the impact of policies. In terms of applications, I am currenlty looking at spatio-temporal problems in social sciences, with a particular focus on crime.
- I am part of the Clean Air project at the Alan Turing Institute. The project aims at developing statistical methodology and machine learning algorithms to support London's Major's office in taking data-driven/evidence based policy decisions in order to improve air quality over the city of London.
- I collaborated with CUSP London on a project aiming at assessing the impact of traffic policies on the number of accidents happening in London.
- Aglietti V., Damoulas T. & Bonilla E. (2018). Log Gaussian Cox Process Networks - arXiv: preprint (submitted)
- Calderoni F., Dugato M., Aglietti V., Aziani A. & Rotondi M. (2017). Price and Non-price Determinants of the Illicit Cigarette Trade: Analysis at the Subnational Level in the EU in Dual Markets: Comparative Approaches to Regulation.
Selected Talks & Posters
March 2018 (Poster): Amazon Machine Learning Workshop, Amazon Research (Berlin, Germany).
The Python code for the published papers will be made available on my GitHub profile.