Room: Engineering Building 2B025

8:30-9:00 Arrivals

9:00-10:30 Introduction to Gaussian Processes Carl Henrik Ek, University of Cambridge.

10:30-11:00 Coffee Break.

11:00-12:30 A second introduction to Gaussian Processes Richard Wilkinson, University of Nottingham.

12:30-13:45 Lunch

13:45-15:30 Lab Session 1

15:30-16:00 Tea Break.

16:00-17:00 Bayesian nonparametric dynamical clustering of time series Adrian Perez Herrero, Universidad Santiago de Compostela

Abstracts

Bayesian nonparametric dynamical clustering of time series

Many systems reveal themselves through repetition a heart beating, a wave arriving at a coast, a machine cycling through its operating modes. The fundamental question is not just what shapes recur, but how frequent they are, how they evolve, and how they relate in sequence. This work introduces the hierarchical Dirichlet Process Gaussian process clustering (HDP GPC), a Bayesian nonparametric model that infers an unbounded number of evolving cluster prototypes from sequential data, with calibrated uncertainty and explicit temporal alignment. Validation on ECG arrhythmia analysis, clinical differentiation of Takotsubo syndrome from myocardial infarction, pedestrian trajectory and ocean wave spectra clustering demonstrates that parsimonious, interpretable clustering can itself become a tool for discovering the underlying phenomena.