International Visiting Professors
The M.Sc. in Stochastics and Data Science features courses modules taught by renowned visiting professors who are internationally recognised leading experts in their respective fields of research.
These activities are partially supported by:
Academic year 2024/2025
- Kolyan Ray (Imperial College London), Fall 2024
Posterior asymptotics in Bayesian nonparametrics in Bayesian Statistics
- Samuel Hermann, Spring 2025
Simulation methods for diffusion processes in Stochastic Processes
- Luis Alberiko Gil-Alana (University of Navarra, Spain), Spring 2025
ARIMA time series and spectral theory in Statistics for Stochastic Processes
Monte Carlo methods for statistical inference in Stochastic Modelling for Statistical Applications On discrete time/space random walks and their diffusion approximations in Stochastic Processes Spectral analysis of time series in Statistics for Stochastic Processes Asymptotics in Bayesian nonparametrics in Bayesian statistics A first introduction to diffusion processes in Stochastic Processes Simulation and inference in Population Genetics in Stochastic Modelling for Statistical Applications Spectral analysis of time series in Statistics for Stochastic Processes Causal machine learning in Statistical Machine Learning Dependent nonparametric priors via completely random measures in Bayesian statistics Fundamentals of Markov chain Monte Carlo methods in Stochastic Modelling for Statistical Applications An introduction to Brownian motion in Stochastic Processes Dynamical processes in complex networks in Complex networks Frequency domain and spectral analysis in Statistics for Stochastic Processes Brownian motion and stochastic integrals in Stochastic Differential Equations Frequentist limits from Bayesian statistics in Bayesian statistics Diffusion processes and boundary classification in Stochastic Processes Simulation and inference in Population Genetics in Stochastic Modelling for Statistical Applications Frequency domain and spectral analysis in Statistics for Stochastic Processes Bayesian inference in high dimensions in Bayesian statistics Franziska Kuhn (Dresden University, Germany), Fall 2020 Some path properties of Brownian motion in Stochastic Differential Equations Albert Milani (University of Wisconsin-Milwaukee, USA), Fall 2020 Distribution theory, Fourier and Laplace transforms in Analysis Probability couplings and Monte Carlo in Stochastic Modelling for Statistical Applications Spectral theory for times series and asymptotics in Statistics for Stochastic Processes Ornstein-Uhlenbeck processes and exit problems in Stochastic Processes Enrico Scalas (University of Sussex, UK), Fall 2019 Heuristic introduction to stochastic differential equations with simulation in Stochastic Differential Equations Botond Szabo (Leiden University, Netherlands), Fall 2019 Asymptotics for posterior Bayesian inference in Bayesian statistics Levy Processes in Stochastic Modelling for Statistical Application Frequency domain and spectral analysis in Statistics for Stochastic Processes Simulation methods for diffusions in Stochastic Processes Scalable algorithms in modern Bayesian computation in Bayesian statistics Topics in branching processes in Stochastic Modelling for Statistical Applications Markov semigroups and diffusion processes in Stochastic Processes Spectral theory for times series and asymptotics in Statistics for Stochastic Processes Dynamical processes in complex networks in Complex networks Computational methods for Bayesian nonparametrics in Bayesian statistics Introduction to martingales in Probability Theory Introduction to stochastic modelling in Population Genetics in Stochastic Modelling for Statistical Applications Dynamical processes in complex networks in Complex networks Brownian motion in Stochastic Processes Spectral theory for times series and asymptotics in Statistics for Stochastic Processes Random partitions and dependent processes in Bayesian nonparametric statistics Diffusion processes and conditioned processes in Stochastic Processes Lévy processes and Poisson random measures in Stochastic Modelling for Statistical Applications