Information Science and Engineering Lab

We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.

Contact Info
CAB F 61.1
Universitaetstrasse 6,
8092 Zurich

+41 44 632 64 96

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Our Focus Areas

Statistical Learning Theory

We study the statistical and algorithmic principles behind learning from various kinds of data. Model selection and the notion of model complexities are key issues in our research.

Inverse Reinforcement Learning

A principled approach in reinforcement learning to design of an appropriate reward function is to tackle the issue by inferring the reward function from recorded demonstrations performed by experts assumed to be acting optimally in a particular environment, thus performing the desired behaviour.

Domain Generalisation

Identifying the right invariance that allows for generalization to utterly unseen domains is crucial for robust deployment of the models in practice and combating distributional shift. We study invariant structures in the solution space that enable knowledge transfer.

Interpretable Machine Learning

Interpretable ML aims to render model behavior understandable by humans, which lies at the heart of human-machine interaction. We are utilizing these explanations for model debugging & fairness, robustness and knowledge discovery.

Our Projects

Selected Projects

About Us

Meet the team

We are data scientist with background in physics, electrical and mechanical engineering, computer science and biology. We work at the intersection of theory and medical applications.






Senior Researchers


Doctoral Students

Group Collaborators and Affiliates