Max Hinne

I am an assistant professor at the Department of Artificial Intelligence, at the Radboud University, Nijmegen, The Netherlands.

There are few objects more interesting than the brain. It has just the right structure and dynamics that results in large-scale information processing, from which our complex cognition emerges and using which it ultimately studies itself.

In my research, I use Bayesian modelling & statistics to learn latent (network) structures in the human brain, via neuroimaging modalities such as fMRI and dwMRI, and to use these as predictive models. I am particularly interested in Bayesian nonparametric models (such as Gaussian processes), in which the complexity of the model is learned from data instead of provided a priori. Other, related, interests incude (quasi-)causal inference, predictive processing, and dynamical systems.

My contact information is listed below.


“All stable processes we shall predict. All unstable processes we shall control.”

John von Neumann

Master thesis projects

Interested in doing a master thesis on topics such as applying or developing new Bayesian nonparametric methods to neuroimaging data? Send me an email!

Publications

PhD thesis

On June 9th 2017, I defended my doctoral thesis entitled Bayesian Connectomics: a probabilistic perspective on brain networks with a cum laude distinction.

Papers

Software

BaCon

The Bayesian Connectomics Toolbox contains Matlab/MEX scripts that implement MCMC approximations for:

Basic usage of each of the scripts is demonstrated in demo.m. Feel free to email me with any questions or comments.

Latent space modeling

Matlab code and Stan models are available for the latent space model that was used for link prediction. Running the sample requires that you have Stan and the MatlabStan interface installed.

GP CaKe

Code for the causal inference / effective connectivity estimation using GP CaKe is available here. There is a tutorial for GP CaKe in the form of a series of blog post, which you can read on the MindCodec website.

Contact information

Online

Email
m.hinne@donders.ru.nl
LinkedIn
http://nl.linkedin.com/in/mhinne
Skype
max.hinne
Google Scholar
Max Hinne

Offline

Nijmegen

Phone

+31-24-3612554

Visiting Address AI

Room MM 02.353
Thomas van Aquinostraat 4
6525 HR Nijmegen

The Bayesian party parrot approves.