Applied Machine Learning

The Applied Machine Learning group aims to develop and apply novel Machine Learning methods for real-world applications. Emerging digitalization allows companies from different fields of industry to produce and collect data from various resources. This is realized by technologies like the Internet of Things (IoT), cyber-physical systems and cloud-computing. All of which is summarized by the fourth industrial revolution, called Industry 4.0, with an increasing demand on research in the area of data analytics. Since these ever-growing amounts of data are difficult to process by conventional methods, machine learning and artificial intelligence provide a powerful and promising approach to handle Big Data. Thus, topics like predictive maintenance, process optimization and process automation benefit from new intelligent algorithms that are developed in the Industry 4.0 environment.

Research areas: Machine Learning, Signal Processing, Wearables, Experimental Studies


Group Head

Dr. Dario Zanca

Room: Room 01.020


Group Members


Current Students

  • Long Do
    Artificial Intelligence trend analysis using speech-to-text data from healthcare podcasts
  • Tassilo Elsberger
    Analysis of Artificial Intelligence Trends In German Economics
    and Politics - A Data-Driven Approach
  • Johannes Jablonski
    Application of data and process analysis techniques for the
    evaluation of agile university projects
  • Christopher Kraus
    Statistical Modeling for Cooperative Positioning with RSSi data (Bachelor's Thesis)
  • Ruining Liu
  • Kirill Menke
    Exploring the Applicability of Mixed Reality for Paramedic Training (Bachelor's Thesis)
  • Daniel Seitz
  • Mengyue Wang
    CAD2Image: Image Synthesis using CAD Models to Augment Training Data (Master's Thesis)
  • Wenyu Zang
    Classification of localized defects on silicon carbide (SiC) wafers using domain adaptation techniques

If you are interested in writing a Bachelor’s or Master’s thesis in our group, please check the lab’s Student Theses and Jobs.


Completed Project