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.

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.

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.

Christian Baumgartner

Bedartha Goswami

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.

Miriam Klopotek

Alina Wernick

  Law, AI, and Society

Alina Wernick is a legal scholar investigating the intersection of law, technology, and society, with a strong foundation in intellectual property law. The Law, AI, and Society Group explores the dynamic relationship between artificial intelligence and legal frameworks, focusing on their broader social and societal impacts.

Kristof Meding

  Computational Law Lab

Kristof Meding’s research combines data-driven machine learning methods with legal research to better understand the interaction between computer science and law. The Computational Law Lab's research focuses on the interaction between Artificial Intelligence and Law. They aim to explore how machine learning methods can be applied in legal sciences.