Mathisca de Gunst

Home page Mathisca de Gunst

Prof.dr. M.C.M. de Gunst
Department of Mathematics
Faculty of Science
Vrije Universiteit Amsterdam
De Boelelaan 1111
1081 HV Amsterdam
e-mail: m.c.m.de.gunst@vu.nl

Research
Courses
Open positions
Software
Links

I am a full professor in the Statistics Group and coordinate our Statistics for Life Sciences research.

Research

My research is in the area of stochastic modeling and statistical analysis of biological processes, and comprises development, assessment and application of statistical models and tools. I use mathematical models ranging from (non)linear regression, Markov and hidden Markov models to non-Markovian counting processes, and frequentist as well as Bayesian estimation techniques. My main interest is in statistical models for networks with a special focus on neuroscience applications.

Current research projects

Statistical inverse problems for network dynamics
Modeling and statistics for gene regulatory networks
Modeling and statistics for brain network dynamics

Past research projects

Statistics for the blood coagulation system
Statistics for neuronal network formation
Modeling the gene network underlying neuronal outgrowth
Statistical analysis of spatiotemporal patterns of activity in neuronal networks
Statistics for ion channel kinetics
Carcinogenesis modeling
Modeling plant cell population growth

Publications
See this page.

Courses

2023-2024:

Seminar in Stochastics
Probability and Statistics (AUC)

Open positions

See this page.

Statistics Group

Staff members
Eduard Belitser
Mathisca de Gunst
Rianne de Heide
Rikkert Hindriks
Frank van der Meulen
Paulo Serra
Wessel van Wieringen
Harry van Zanten

PhD students
Marc Corstanje
Marina Dietrich
Adel Magra
Luminita Maxim
Dennis Nieman
Alexandra Vegelien

Software

Some software developed by our group

Cov3KP (Ros, 2015):
algorithm for estimating the covariance matrix of a matrix variate normal model where the covariance matrix is a Kronecker product of three components.
Accompanying paper.
GEMULA (Geeven, 2012):
method for identification of context-specific gene regulatory networks based on linear models and parameter estimation using lasso.
Accompanying paper.
LLM3D (Geeven, 2011):
log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data.
Accompanying paper.