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. To effectively handle this big data, Deep Learning and Signal Processing techniques are needed to provide powerful and promising solutions.

 

Group Head

Dr. Dario Zanca

Room: Room 01.016

Group Members

Students

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.

  • Amlan Jyoti Buragohain
    Machine Learning for a plug-and-p/ay Digital Twin of the production resource
  • Asif Haris
    Quantum Circuit Optimization via Hierarchical Reinforcement Learning
  • David Rock
    Using Smart Devices to Assess the Health Status of Palliative Care Patients by Monitoring Activities of Daily Living
  • Huang Haiting
    Comparing Performance and Transparency of Multimodal Foundation Models in Healthcare
  • Zhao Kai
    Automated Pose Estimation of Mice Paws
  • Michael Girstl
    Safe Reinforcement learning using Successor representations

  • Nils Steinlein
    The creation and evaluation of a video-based polar bear Re-Identification dataset on current Re-ID methods
  • Sara Zarifi
    Lynx Re-Identification from Camera Trap Images in the Wild

 

Projects

2025

2024

2023

2022

2021

2020

2019

2017

2016

2015