Prof. Dr. Bjoern Eskofier

Machine Learning and Data Analytics Lab

The researchers in the Machine Learning and Data Analytics (MaD) lab conduct theoretical and applied research for wearable computing systems and machine learning algorithms for engineering applications at the intersection of sports and health care. Our motivation is generating a positive impact on human wellbeing, be it through increasing performance, maintaining health, improving rehabilitation, or monitoring disease.


Prof. Dr. Daniel Roth

Professorship for Human-Centered Computing and Extended Reality

Our research is centered around the exploration of technological solutions and interaction metaphors for human-machine interfaces that improve the way we work, interact, and communicate. A majority of our research considers Extended Reality user interfaces in the context of physical and mental health, such as Virtual-, Mixed- or Augmented Reality systems.


Prof. Dr. David B. Blumenthal

Biomedical Network Science Lab

The Biomedical Network Science (BIONETS) lab investigates molecular disease mechanisms using techniques from network science, combinatorial optimization, and artificial intelligence. We develop algorithms and tools to mine multi-omics data for such mechanisms and to individuate novel strategies for mechanistically grounded drug repurposing and causally effective treatments of complex diseases. We also develop privacy-preserving decentralized biomedical AI solutions, which enable cross-institutional studies on sensitive data.


Quelle: FAU/Georg Pöhlein

Prof. Dr. Katharina Breininger

Artificial Intelligence in Medical Imaging Lab

The Artificial Intelligence in Medical Imaging (AIMI@FAU) lab aims at combining machine learning approaches and medical imaging for different applications. A particular focus is on understanding what data is needed for efficient learning, robustness, and transferability. In particular, we are interested in applications in interventional imaging, multimodal data, and digital pathology.


Prof. Dr. Andreas Kist

Artificial Intelligence in Communication Disorders (anki lab)

Our lab focuses on the development and application of artificial intelligence in medicine, particularily in communication disorders. Our main aim is to provide generalized, clinical applicable solutions to support diagnosis and therapy monitoring. To achieve this, we use genuine approaches to optimize deep neural networks and mine novel AI-specific hardware accelerators. We encourage open science and provide an environment allowing students to thrive.


Prof. Dr. Alessandro Del Vecchio

Neuromuscular Physiology and Neural Interfacing (N-squared) lab

The Neuromuscular Physiology and Neural Interfacing (N-squared) laboratory aims at deciphering neuromuscular function and developing human-machine interfaces for neurorehabilitation, restoring of motor function, and motor augmentation. Our lab activities are focused on the acquisition and analysis of neural signals with high spatial and temporal resolution (high-density arrays) from the central and peripheral nervous system in healthy volunteers and in subjects with motor impairments.


Prof. Dr. Tobias Reichenbach

Chair of Sensory Neuroengineering

The multidisciplinary research team of the Chair of Sensory Neuroengineering combines methods from artificial intelligence with computational neuroscience and neuroimaging to advance our understanding of the neural processing of complex natural signals. Research findings are applied to develop novel bio-inspired technology as well as towards better diagnosis and rehabilitation of neurological impairments.


Prof. Dr. Seung Hee Yang

Artificial Intelligence in Biomedical Speech Processing

Our research focuses on speech and language-inspired AI in biomedical engineering. We mainly use digital speech signal processing and machine learning to build computational models, which are then used to solve real-world problems in digital health applications. At the intersection of speech and healthcare, we perform innovative research with the university clinic and engineering, in order to discover more interesting problems in intelligent digital healthcare.