Search staff/organisations dr. CFW Peeters
Name
Namedr. CFW Peeters
FirstnameCarel
Emailcarel.peeters@wur.nl

Job details
DescriptionAssociate Professor
OrganizationDepartment of Plant Sciences
Organization UnitMathematical and Statistical Methods - Biometris
Phone+31 317 485 912
Mobile
Secretarial phone+31 317 484 085
Phone 2
Fax
Note for telephonist
Note by telephonist
Visiting addressDroevendaalsesteeg 1
6708PB, WAGENINGEN
Building/Room107/N.A.
Postal addressPostbus 16
6700AA, WAGENINGEN
Courier3

Biography

Education
Carel F.W. Peeters (Nijmegen, 1982) obtained an M.Sc. (with great distinction) in Statistics, specializing in Mathematical Statistics and Applied Mathematics for the Behavioral & Life Sciences, from the Leuven Statistics Research Center (LStat) at the Katholieke Universiteit Leuven (KUL). He obtained his Ph.D. in Bayesian Statistics at Utrecht University (UU), with a concentration in Bayesian theory and computation for constrained inference problems. His dissertation has won multiple awards.

Appointments
While working on his Ph.D. thesis he held a research fellowship at the Strategic Chair Integrity of Governance at VUA as well as a position as research scientist at the Psychometric Research Centre of CITO, Institute for Educational Measurement. In 2012, he became a postdoctoral researcher and subsequently Assistant Professor in Biostatistics at the Department of Epidemiology and Data Science of the Amsterdam University Medical Centers (AUMC). As of January 2021, he is an Associate Professor of Statistics at the department of Mathematical & Statistical Methods of Wageningen University & Research. 

Research
His research focuses on the interface of high-dimensional multivariate statistics, statistical machine learning, and complex data. High-throughput technology and data-stream mining algorithms produce data characterized by small samples and massive numbers of features. He thus focuses on developing methods for the analysis of such high-dimensional data. Especially methods that are able to automatically infer a possible data-generating mechanism. In doing so, he tries to balance theory and computation for effective application.


Expert Profile
Expertise
Social media
  Personal webpage
  ResearchGate
  arXiv
  GitHub

Publications
Publication lists
Researcher ID's

Education
Courses
  • MAT-32806 - Statistics for Data Scientists
  • MAT-34806 - Bayesian Data Analysis
  • MAT-70424 - MSc Internship Mathematical and Statistical Methods
  • MAT-79324 - MSc Research Practice Mathematical and Statistical Methods
  • MAT-80424 - MSc Thesis Mathematical and Statistical Methods
  • MAT-80436 - MSc Thesis Mathematical and Statistical Methods
Caption Text
  • mail
  • chat
  • print

Profiel