Welcome
Welcome to the MaD 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.
The MaD Lab is the home of the Innovation Lab for Wearable and Ubiquitous Computing, where students develop innovative prototypes with industry partners.
If you are a young life scientist looking to acquire crucial skills to transition to the working world, SCI FI can give you the skills you need to thrive as an innovator in the healthcare industry.
There is still time to register for our informative webinar on the 19.05.21 at 5 pm CEST
https:/... Continue
We are proud to announce that our last paper “Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks” has been accepted for publication at the annual International Joint Conference on Neural Networks (IJCNN), the flagship conference of the IEEE Computational Intelligence Society and... Continue
Thomas, who grew up in the Bavarian alps near Berchtesgaden, received both his bachelor's and master's degree in computer science at the Friedrich-Alexander-University Erlangen-Nürnberg (FAU). In April 2021 he joined the MaD team, where he is working to improve breast cancer prevention and treatment... Continue
Madeleine joined the MaD-Team in April 2021 as a Ph.D. candidate. After completing her Bachelor's studies in Biomedical Engineering at the technical university in Regensburg she decided it was time for a change of scenery and moved to Erlangen for her master's degree in Medical Engineering at the FA... Continue
The Machine Learning and Data Analytics Lab (MaD Lab) is proud to announce an intro session in which we will present numerous research projects (10 ECTS), in a wide range of applications, from deep learning methods to industrial or medical applications.
The meeting will take place Thursday, April... Continue
