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.
«Home of the Innovation Lab»
The MaD Lab is the home of the Innovation Lab for Wearable and Ubiquitous Computing, where students develop innovative prototypes with industry partners.
The registration for the Innolab in thewinter term 2018/19 starts on the 17th of September! Students can register via Studon!
Given his passion for computers it was obvious for Jonas to start an apprenticeship as IT specialist after graduating Realschule. Throughout the next three years he will assist with technical support for computers and servers and help to improve the IT infrastructure within the Machine Learning and ... Continue
Each year, the Bavarian Ministry for Science and Arts (Bayerisches Staatsministerium für Wissenschaft und Kunst) awards five women for her graduation in engineering sciences. This year a member of the MaD lab, Marlies Nitschke , was nominated by the FAU and finally won the price for her excellent Ma... Continue
Did you ever wonder what we are actually doing in the MaD Lab? Next week you will have the chance to get insights into our Ph.D. students' work.
The MaD Lab will host the bi-annual MaD-Conference where the Ph.D. students present their recent topics.
For more information on the topics please se... Continue
An studied Electrical Engineering at the Technical University of Berlin. During that time, he spent a year at the KTH Royal Institute of Technology in Stockholm, Sweden and did a Master program in Electrical and Computer Engineering at the University of Michigan, Ann Arbor, US. The time abroad shift... Continue
Congratulations to Dr. MaD Felix Kluge, who successfully defended his PhD thesis entitled "Instrumented gait analysis in osteoarthritis: From lab towards ambulatory systems".
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