Søren Føns Vind Nielsen presented a poster on “Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data” at the 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner.

Tue Herlau presented a poster on “Completely random measures for block-structured networks” at the NIPS workshop on Bayesian Nonparametrics: The Next Generation.

Mikkel N. Schmidt presented the paper “Numerical approximations for speeding up MCMC inference in the infinite relational model” at EUSIPCO 2015.

Rasmus Røge presented the poster “Unsupervised Segmentation of Task Activated Regions in fMRI” at the Machine Learning for Signal Processing 2015 in Boston.

Morten Mørup gave a talk at ICML workshop on Statistics, Machine Learning and Neuroscience (Stamlins 2015) on Non-parametric Bayesian Modeling of Functional and Structural Brain Connectivity.

The poster on “Quantifying Temporal States in rs-fMRI Data using Bayesian Nonparametrics” was presented at HBM2014. To see the poster click here.

The NeuroImage article “Non-parametric Bayesian graph models reveal community structure in resting state fMRI” is available from:
http://www.sciencedirect.com/science/article/pii/S1053811914004686.

The paper “Discovering Hierarchical Structure In Normal Relational Data” was presented at Cognitive Information Processing (CIP2014), http://cip2014.conwiz.dk/home.htm#.U6AP0_l_t3E

Paper accepted at PRNI 2014 on modeling structural connectivity in full image resolution.

The following two abstracts will be presented at OHBM 2014:

Josefine Korzen, Kristoffer Hougaard Madsen, Morten Mørup, Quantifying Temporal States in rsfMRI Data using Bayesian Nonparametrics.

Kasper Winther Andersen, Kristoffer H. Madsen, Hartwig Roman Siebner, Mikkel N. Schmidt, Morten Mørup, Lars Kai Hansen, Community structure in resting state complex networks.

Paper accepted for the IEEE International Workshop on Machine Learning for Signal Processing 2013 on a high performance Gibbs sampler for relational modeling of complex networks.