Matthias Hüser, MSc. ETH Computer Science

"Reality is that which, when you stop believing in it, doesn’t go away." -- Philip K. Dick (1928-1982)

PhD Student

E-Mail
matthias.hueser@get-your-addresses-elsewhere.inf.ethz.ch
Phone
+41 44 632 23 71
Address
ETH Zürich
Department of Computer Science
Biomedical Informatics Group
Universitätsstrasse 6
8092 Zürich
Room
CAB F53.1

I am broadly interested in Machine Learning and Signal Processing for Healthcare, in particular the application of Deep Learning and Bayesian non-parametric methods to EHR and genomic data.

Before joining the Rätsch Lab, I studied Computer Science at ETH Zürich (MSc) and Computing at Imperial College London (BEng). Previously I have worked on forecasting intracranial hypertension and cerebral hypoxia events using high-frequency ICU data.

Abstract Human professionals are often required to make decisions based on complex multivariate time series measurements in an online setting, e.g. in health care. Since human cognition is not optimized to work well in high-dimensional spaces, these decisions benefit from interpretable low-dimensional representations. However, many representation learning algorithms for time series data are difficult to interpret. This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time. To address this problem, we propose to couple a variational autoencoder to a discrete latent space and introduce a topological structure through the use of self-organizing maps. This allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance. Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the latent space. This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty. We evaluate our model on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application. In the latter experiment, our representation uncovers meaningful structure in the acute physiological state of a patient.

Authors Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch

Submitted Arxiv

Link

Authors Valeria De Luca, Matthias Hüser, Martin Jaggi, Walter Karlen, Emanuela Keller

Submitted 16th International Symposium on Intracranial Pressure and Neuromonitoring, Cambridge, MA, USA

Link

Authors Matthias Hüser, Valeria De Luca, Martin Jaggi, Walter Karlen, Emanuela Keller

Submitted Vasospasm - 13th International Conference on Neurovascular Events after Subarachnoid Hemorrhage

Link