Prof. Dr.-Ing. Matthias Luther

• February 2018 – July 2019
  Postdoctoral researcher at the Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
• October 2012 – January 2018
  Ph.D. student at the Machine Learning and Data Analytics Lab / Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
• October 2010 – August 2012
  M.Sc. in computer science at the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
• October 2007 – September 2010
  B.Sc. in computer science at the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

• Quantitative Dehydration Estimation During Physical Exercise
  Quantitative estimation of dehydration (total body water loss) during physical exercise using bioimpedance measurements, temperature measurements, salivary samples, and sweat samples.

• Bluetooth Controller Support for UE4 GoogleVR iOS Apps
  An Unreal Engine 4 (UE4) plugin to enable usage of bluetooth remote controllers / gamepads / joysticks in iOS virtual reality (VR) apps.
• Embedded Classification Software Toolbox (ECST)
  A software toolbox to train machine learning and classification systems, to compare accuracy and performance, and to analyze the complexity of trained systems.
• Visual Field Assessment and Training (VFAT): Smartphone Virtual Reality (VR) App for Mobile and Home Perimetry
  Unreal Engine 4 (UE4) based virtual reality (VR) smartphone app for mobile and home perimetry.
• Visual Field Assessment and Training (VFAT): Server for Mobile and Home Perimetry
  Django based server backend for mobile and home perimetry.

No publications found.

• Data Mining in the U.S. National Toxicology Program (NTP) Database
  This project investigated if effects on rodents’ liver weight in short-term studies can be exploited to predict the incidence of liver tumors in long-term studies.
• Digital Vision Trainer
  This project builds digital, visual perceptual learning systems for 3D stereo monitors as well as virtual reality headsets.
• Green Belt ML@Operations
  This project develops a seminar on applied machine learning for engineers in production and quality.
• Noninvasive Determination of the Human Hydration Level
  This project proposed several machine learning approaches for quantitative dehydration estimation during physical exercise.
• Theoretical Machine Learning
  This project summarizes theoretical contributions to machine learning research.

• 1st International Symposium on Human Hydration Monitoring Technologies at BSN2017 (co-chair with Karl E. Friedl)
  This symposium considered technological, physiological, and data-analytics aspects of hydration monitoring that have been explored by researchers with first-hand experience in a range of applications from clinical patients to active athletes.