General

Research

Teaching

Welcome to the homepage of Kurt Barbé. I am professor at the department of mathematics (DWIS) and the department of public health (GEWE) of the Vrije Universiteit Brussel (VUB). At DWIS, I am a member of the research group Digital Mathematics (DIMA). At GEWE, I am member of the research group Biostatistics and Medical Informatics (BISI) My coordinates are: Campus Etterbeek, Building G, Floor 6, Office 310. At the medical campus, my office can be reached in Building D, Floor 1, Office 134. I can be reached through email (kurt.barbe@vub.ac.be) and phone (DWIS: 02/629.34.94 and GEWE:02/477.47.24).

 

You find my publications and citation record through Google Scholar here. My PhD thesis is found here.

 

Vacancies

 

PhD position in the field of computational (bio)statistics on the following topic. Candidates with an interest in (i) decision support systems, (ii) cancer radiotherapy, (iii) quantitative techniques are invited to apply. The PhD position aims at obtaining a PhD degree in medical sciences or mathematics. The ideal candidate holds a master degree in (i) exact sciences, (ii) engineering or (iii) biomedical or pharmaceutical sciences. Please apply by contacting me through email with (i) motivation letter, (ii) transcript of your studying results per subject, (iii) copy of your relevant degree and finally (iv) CV.

 

 

General

Research

Teaching

My research interests are currently in 4 subdomains of biostatistics: (i) fractional order time series analysis for bio-impedance spectroscopy, (ii) Rician distributed general linear models for functional MRI, (iii) Non-parametric contrast tests for ANOVA designs in animal trials and (iv) random regression forests for generalized linear models for clinical decision support systems.

 

The first track deals with long memory processes where the memory of the process is polynomially fading instead of exponentially. This is particularly useful for modelling diffusion and dispersion phenomena in the human body where our primary application is in the field of electro-surgery. The second track is oriented towards modelling the brain haemodynamics under low signal-to-noise conditions in fMRI measurements. Under low SNR conditions the data do no longer satisfy the Gaussian paradigm and show a strong right skewed behavior, the distribution is known as the Rice distribution. In the third research track, we acknowledge that contrast tests achieve a high power. Unfortunately non-parametric contrast tests are limited to the Jonckheere-Terpstra test or the Arnoldi-Akriatis rank randomization tests. Neither of these are direct generalizations of the contrast t- or f-tests in parametric statistics. Therefore proper alternatives are worth researching. In the fourth research track, we consider machine learning and artificial intelligence techniques popping up in data-science and analysis. Random regression forests are an interesting non-parametric technique to model high dimensional data. Its properties when applied to generalized linear models like survival analysis, logistic regression and Poisson regression is not fully explored nor understood. For applications in cancer radiotherapy random regression forests are applied and its behavior and properties are studied.

 

In my group, the research is dedicated towards advances in mathematical statistics with attention to (i) computational properties and algorithms, (ii) user-friendliness and applicability of the techniques. Although the goals are mathematical innovations to advance the field of biostatistics, the different research tracks are always data-driven and the open problems considered are application and practical inspired from medicine. Therefore specific care is foreseen in our research team to provide user-friendly methods which can be applied by the layman in biomedical research.

 

 

General

                               Research    

                    Teaching

My teaching as well as my research is situated in applied mathematics with a strong emphasis on statistics. I teach in the faculty of Science and the faculty of Medicine. An overview of my current teaching task is as follows:

 

o   Faculty of Sciences

 

1.      Scientific computing (Wetenschappelijk rekenen): 1st Bachelor Mathematics / 3rd Bachelor Computer Science

2.      Harmonic analysis (Harmonische analyse): 1st Master Mathematics

3.      Statistical Methods 1 (Statistische methoden 1): 1st Master Mathematicssyllabus (Eng.)

4.      Statistical Methods 2 (Statistische methoden 2): 2nd Master Mathematicssyllabus (Eng.)

 

o   Faculty of Medicine

 

1.      Mathematics (Wiskunde): 1st Bachelor Pharmaceutical/Biomedical Sciences – syllabus (Du.)

2.      Biostatistics (Biostatistiek): 2nd Bachelor Pharmaceutical/Biomedical Sciences

3.      Quantitative analysis and data-processing (kwantitatieve analyse en dataverwerking): 1st Master in Health Sciences and Management

4.      Power analysis for animal trials – slides (Du.)