Michael Eickenberg

Postdoc in the gallantlab at UC Berkeley.

My work consists in using machine learning methods towards forward and reverse modeling of fMRI brain activity following sensory stimulation.
The main approaches I take to create predictive models from and to BOLD fMRI brain imaging data lie in regularized empirical risk minimization methods, often with non-smooth convex regularizers, which lead to convex optimization problems for which iterative algorithms can be devised.
Recently I have had success in forward modelling brain activity from features extracted from convolutional networks. This project was one of the reasons to create sklearn-theano, an open-source software package which makes the use of powerful convolutional nets very easy. It has also sparked my general interest in this type of learning architecture.

Open Source Contributions


Teaching assistant for


(a one element list, so I can easily add others as they come streaming in by the dozens)


LinkedIn Twitter @meickenberg Github @eickenberg
Michael staring at two screens
(me staring at brains,
© Inria / Photo H. Raguet)