Main research interests
The Statistics group develops and compares data-analytical methods for analyzing and sampling complex structured correlated data sets. It includes parameter estimation, model fitting, latent variable models, mixed models, missing data, statistical process control, survival & reliability theory, time series analysis, and statistical learning methods.
One of the central themes is the analysis of high- dimensional temporal data sets and other large data sets. The group actively explores new research lines in Data Science and maintains many strong ties with industry, including biopharmaceutical companies, chemical industry, and medical centers and international research institutes.
Framingham Heart Study: A long-term longitudinal data set on more than 5000 participants (started at 1948) has been brought to the TU/e to collaborate with Boston University on new statistical methods.
Strong collaboration with the Academic partners of Maelstrom Research, a leading international institute on harmonization of data from multiple cohort studies. Industrial collaboration with pharmaceutical industry on validation and implementation of rapid microbiological methods to test medicinal products and processes. Continuous Personal Health, as part of the Philips flagship Data Science , is currently developing new methods for monitoring heart characteristics and sleep. We are leading a Big Data & Reliability Platform together with industrial partners.