Fabio Sigrist

I am a Senior Scientist at the Seminar for Statistics at ETH Zurich.

Contact Information

Fabio Sigrist
Seminar for Statistics
ETH Zurich, HG G 24.2
Rämistrasse 101
8092 Zurich, Switzerland
Email: fabio (dot) sigrist (at) stat (dot) math (dot) ethz (dot) ch

Publications

See Google Scholar

Selected publications

• Sigrist, F., Künsch, H. R., & Stahel, W. A. (2012). A dynamic nonstationary spatio-temporal model for short term prediction of precipitation. Annals of Applied Statistics
• Sigrist, F., Künsch, H. R., & Stahel, W. A. (2015). Stochastic Partial Differential Equation Based Modelling of Large Space–Time Data Sets. Journal of the Royal Statistical Society Series B: Statistical Methodology
• Audrino, F., Sigrist, F., & Ballinari, D. (2020). The impact of sentiment and attention measures on stock market volatility. International Journal of Forecasting
• Sigrist, F. (2021). Gradient and Newton boosting for classification and regression. Expert Systems With Applications
• Sigrist, F. (2022). Gaussian Process Boosting. Journal of Machine Learning Research
• Sigrist, F. (2023). Latent Gaussian Model Boosting. IEEE Transactions on Pattern Analysis and Machine Intelligence
• Sigrist, F., & Leuenberger, N. (2023). Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities. European Journal of Operational Research
• Kündig, P., & Sigrist, F. (2025). Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models. Journal of the American Statistical Association
• Kündig, P., & Sigrist, F. (2025+). A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios. European Journal of Operational Research (to appear)
• Gyger, T., Furrer, R., & Sigrist, F. (2025+). Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data. SIAM/ASA Journal on Uncertainty Quantification (to appear)

Software

See GitHub

Selected software

• GPBoost
  • C++ library with Python and R interfaces for tree-boosting, mixed effects models, and Gaussian processes
  • 500+ stars on GitHub

For ETHZ students looking for an MSc thesis topic

I can offer master thesis topics in the area of statistical machine learning (e.g., Gaussian processes, tree-boosting, neural networks, spatio-temporal statistics, applications in environmental sciences and finance). A selection of concrete topics can be requested by email.