Statistical Science Laboratory

Research

The research interests of the Statistical Science Laboratory encompass a wide range of advanced topics in statistics and machine learning. These include networks, point processes, Bayesian inference, high-dimensional and nonparametric statistics, tree algorithms, and graphical models.

Additionally, the lab is dedicated to improving the interpretability, robustness, and fairness of statistical and machine learning methods. Significant efforts are directed towards counterfactual and Bayesian analysis to develop methodologies that are not only powerful and accurate but also transparent, reliable, and equitable. This comprehensive approach aims to create tools and frameworks that can be trusted and effectively applied in various domains.

Competence Area

  • Statistical Network and Graphical Models
  • High-dimensional and nonparametric statistics
  • Tree algorithms
  • Point Processes
  • Interpretability, Robustness and Fairness of Machine Learning Methods

Open Positions: 

PhD position in Statistics/Machine Learning on spatio-temporal Hawkes processes and their applications.

Application Deadline: 28 February 2025.

We invite applications for a fully-funded Ph.D. position for conducting methodological research in the field of spatiotemporal point processes. Point processes such as Hawkes processes have been widely used to model events in various domains such as finance, epidemiology, social network analysis, and neuroscience. The meth- ods will be applied to real-world innovative datasets

The future student will be enrolled in the Ph.D. program in Computational Science at USI (gross annual salary: 48,000-51,000 CHF), supervised by Prof. Deborah Sulem and co-supervised by Prof. Xenia Miscouri- dou (University of Cyprus and Imperial College London).

Click here for the full PhD Description.