Teaching/Projects
This semester (Spring 2026), I am head teaching assistant for Multivariate Statistics (MATH-444).
Solutions to the weekly practicals are available on my GitHub.
Previously, I have TAd for:
- Multivariate Statistics (Spring 2024, 2025, 2026)
- Introduction to Statistics (Spring 2022, 2023)
- Statistical Theory (Winter 2020, 2021, 2022)
- Applied Bioinformatics (Spring 2021)
Supervision
I have had the pleasure of supervising several Bachelor’s and Master’s theses and projects in Statistics and Machine Learning.
To get a feel for the kind of projects I enjoy supervising, here are a few highlights, followed by a comprehensive list.
Spectral Light Exposure Timeseries
Statistical analysis of spectral light timeseries data. Employed optimal transport and designed a neural network architecture (encoder–decoder–classifier) to cluster measured spectra, classify spectral types, and decompose signals into source components.
Stochastic Interpolants in RKHS
Wasserstein gradient flows for sampling from complex distributions by pulling back Gaussian stochastic interpolants in RKHS.
COVID-19 Dynamics via Optimal Transport
Used optimal transport to register infection curves and quantify the effect of governmental restrictions on COVID-19 cases across the USA via vector-on-vector regression. Focused on time alignment and phase–amplitude separation.
| Level | Name | Year | Title |
|---|---|---|---|
| Master's thesis | Luca Raffo | 2026 | Embedded stochastic interpolants: generative modelling in feature space |
| Master's thesis | Francesco Bellotto | 2026 | Schrödinger bridges on the Cholesky manifold: score matching for functional/structural connectivity matrices |
| Master's thesis | Fahim Beck | 2023 | Statistical Analysis and Deep Learning for Spectral Light Exposure Timeseries Data |
| Master's thesis | Francesco Tripoli | 2023 | Time Dynamics of COVID-19 Using OT: Assessing the Impact of Restrictions |
| Master's project | Luca Raffo | 2025 | Wasserstein Gradient Flows: Sampling and Diffusions |
| Master's project | Beji Qayis | 2023 | Computational Methods for Optimal Transport |
| Master's project | Maxence Robaux | 2022 | Forecasting Electricity Consumption with Functional Time Series |
| Bachelor's project | Pierre-Gabriel Meyrignac | 2024 | Introduction to Empirical Processes |
| Bachelor's project | Lucas Poinsignon | 2024 | Bootstrap Principles and Edgeworth Expansions |
| Bachelor's project | Leonardo Barbieri | 2021 | Functional PCA with Application to COVID-19 Data |