Independent Research Groups established by the Cluster

Franz Baumdicker

Mathematical and Computational Population Genetics

Franz Baumdicker's research focuses on mathematical models for the evolution of microbes. His group investigates how machine learning can leverage phylogenetic information in population genetics.

Christian Baumgartner

Machine Learning in Medical Image Analysis

Christian Baumgartner's research is at the interface of machine learning and automated medical image analysis with the goal to create safe and robust clinical prediction systems.

Konstantin Genin

Epistemology and Ethics of Machine Learning

Konstantin Genin is interested in learning-theoretic approaches to issues in the ethics and methodology of statistics and machine learning.

Bedartha Goswami

Machine Learning in Climate Science

Bedartha Goswami's research aims to investigate climate processes and unravel the complexity of climatic systems with tools and techniques from the wide domain of machine learning.

Nicole Ludwig

Machine Learning in Sustainable Energy Systems

Nicole Ludwig's research focuses on probabilistic machine learning seeking to understand the role of uncertainty in future sustainable energy systems.

Claire Vernade

Lifelong Reinforcement Learning

The Lifelong Reinforcement Learning Lab studies interactive machine learning problems where feedback loops and long-term impact of actions must be taken into account to train agents. In particular, we want to build agents who anticipate changes in the environment and show adaptive and sample-efficient behaviours that improve with experience.

Charley Wu

Human and Machine Cognition Lab

Charley Wu’s research studies the specific shortcuts and cognitive algorithms that people use to make inference tractable. His work seeks to narrow the gap between human and machine learning.

Early Career Research Groups established by the Cluster

Katharina Eggensperger

Automated machine learning for Science

Katharina Eggensperger researches how to make machine learning easily accessible and more efficient through automated machine learning (AutoML) to advance and augment scientific research.