I am interested in causal inference and its potential to provide novel insights in neuroimaging. We have provided a comprehensive set of causal interpretation rules for encoding and decoding models in neuroimaging studies (see this publication and this explainer video (5 min)). Currently I am working with Arthur Gretton and Moritz Grosse-Wentrup on causal effect recovery from linear mixtures (see MERLiN and this manuscript).Since 2015/11 I am a PhD student at the Max Planck Institute for Intelligent Systems, supervised by Bernhard Schölkopf and Moritz Grosse-Wentrup. Throughout my studies I have been working there as research assistant. I obtained my MSc in Computational Statistics and Machine Learning from University College London, funded by the German National Academic Foundation (Studienstiftung) and the German Academic Exchange Service (DAAD), and my BSc in Mathematics from the University of Tübingen.
I am happy to receive criticism, comments or helpful suggestions via email: mail AT sweichwald DOT de .
MERLiN is a causal inference algorithm that can recover from an observed linear mixture a causal variable that is an effect of another given variable. MERLiN implements a novel idea on how to (re-)construct causal variables and is robust against hidden confounding.arXiv / code / doi / pdf
Pymanopt lowers the barriers to users wishing to use state of the art manifold optimization techniques, by using automated differentiation for calculating derivative information, saving users time and saving them from potential calculation and implementation errors.
(Example: manifold optimisation for inferring parameters of a MoG model.)
We provide a set of rules which causal statements are warranted and which ones are not supported by empirical evidence. Especially, only encoding models in the stimulus-based setting support unambiguous causal interpretations. By combining encoding and decoding models, however, we obtain insights into causal relations beyond those that are implied by each individual model type.arXiv / doi / explainer video (5 min)
This paper proposes an extension of the MERLiN algorithm to identify non-linear cause-effect relationships between linearly mixed neuroimaging data.arXiv / code / doi / pdf / slides
In this paper, we argue that it is not sufficient to distinguish between encoding- and decoding models: The interpretation of such models depends on whether they are employed in a stimulus- or response-based setting.arXiv / doi / pdf
In this work it is shown that index finger positions can be differentiated from non-invasive EEG recordings in healthy human subjects. Among the different spectral features investigated, high β-power (20–30 Hz) over contralateral sensorimotor cortex carried most information about finger position.arXiv / doi / pdf
This work suggests that the layer-wise similarity of feature representations in biological and artificial neural networks is a result of optimal coding that enables robust transmission of object information over noisy channels. Our work further provides a plausible explanation why optimal codes can be learned in unsupervised settings.arXiv / pdf
In my opinion research results should be accessible to everyone. If not publishing open access, authors should consider posting preprints or accepted manuscripts on arXiv or their personal website. The SHERPA/RoMEO database makes it easy to check a journal's/publisher's policy and decide on posting a preprint or author accepted manuscript.
2015. Consider a randomised instrumental variable S and another pre-specified variable X. Causal effect recovery from linear mixtures aims to identify Y from an observed linearly mixed signal such that S→X→Y. We present five related algorithms for recovery of causal effects even in the presence of hidden confounders. Evaluation on both synthetic and EEG data indicates usefulness and applicability of our method.pdf
2013. This seminar paper reviews Kurt Gödel's article »What is Cantor's continuum problem?«. As this paper aims to be almost self-contained, short recaps, rough explanations and selective examples are provided where appropriate.pdf
2011. A few small scripts which allow to simulate the ant's behaviour within different two-dimensional grids with different kinds of borders. The ant is represented by a little red triangle which allows to indicate the current direction. One can follow the ant move by move, step by step or in fast-forward mode.code