Our research focuses on the development of machine learning methods in the context of responsible AI, particularly in biometric applications such as face recognition. Our goal is to develop learning algorithms that are technically robust, ethically fair, legally compliant, and under human control. We investigate how AI systems, and machine learning systems in particular, can be designed to be fairer, more privacy-friendly, more reliable, more explainable, and more secure, both in the biometric context and beyond.

In our teaching, our goal is to equip students with both the fundamentals and advanced methods of responsible AI, and in particular, machine learning. In doing so, we aim to prepare our students to actively and successfully shape a data-driven future.

AI systems offer great opportunities for the economy and individuals, but at the same time pose significant risks. These systems are never neutral, rather, they embody the values of the people who develop and use them. Responsible design of these systems is therefore essential to contributing to a digitally and socially sustainable society guided by shared values.

Our research focuses on two interconnected areas: Responsible AI and biometric systems. Responsible AI aims to embed ethical principles, legal compliance, and public trust directly into the development and operation of AI models. Biometric systems enable the automatic verification of a person’s identity based on behavioral or biological characteristics, such as fingerprints or facial recognition. These technologies are among the most accurate and influential AI systems for identity verification, access control, and personalized services in today’s digital world.

In our teaching, our goal is to equip students with both the fundamentals and advanced methods of responsible AI, and in particular, machine learning. In the bachelor’s program, the course “Machine Learning and Artificial Intelligence” introduces students to key learning methods and their practical application, laying a broad foundation. In the master’s program, students build upon these foundations through seminars and project work, gaining specialization in research-oriented knowledge in courses such as “Advanced Learning Strategies for AI Systems,” “Applied AI Prototyping,” and “Strategy Mining.”

In the Machine Learning Lab I and II (bachelor) and Responsible AI Lab (master) courses, students work in small groups on research questions drawn from current research. Through weekly discussions with their supervisors, they develop new solutions, design and conduct experiments, and analyze the results. The topics addressed in these labs can be continued in a final thesis by mutual agreement. Of course, we also offer our own thesis topics.

Our goal is to prepare our students to actively and successfully shape a data-driven future.

Winter Semester

  • Machine Learning & Artificial Intelligence (Lecture & Exercise)
  • Machine Learning Lab I (Project)
  • Biometrics & AI (Seminar)
  • Wissenschaftliches Arbeiten und Seminar (Seminar)

Summer Semester

  • Machine Learning Lab II (Project)
  • Wirtschaftsinformatik-Projekt (Project)
  • Datenintegration und Datenanalyse (Lecture & Exercise)

Winter Semester

  • Strategy Mining (Project)
  • Responsible AI Lab (Project)

Summer Semester

  • Introduction to Machine Learning (Lecture & Exercise)
  • Advanced Learning Strategies for AI Systems (Seminar)
  • Applied AI Prototyping (Project)
  • Biometric Recognition (Lecture & Exercise)

Here is a list of the most recent courses in Jogustine, organized by semester:

Winter Semester

  1. Biometrics & AI
    Instructor: Philipp Terhörst
  2. Machine Learning and Artificial Intelligence
    Instructor: Philipp Terhörst
  3. Machine Learning and Artificial Intelligence (Übung)
    Instructor: Philipp Terhörst
  4. Machine Learning Lab I
    Instructor: Philipp Terhörst
  5. Responsible AI Lab – Übung 1
    Instructor: Philipp Terhörst
  6. Responsible AI Lab – Übung 2
    Instructor: Philipp Terhörst
  7. Strategy Mining
    Instructor: Philipp Terhörst

WiSe 2026/27

Summer Semester

  1. Advanced Learning Strategies for AI Systems
    Instructor: Philipp Terhörst
  2. Applied AI Prototyping
    Instructor: Philipp Terhörst
  3. Maschinelles Lernen und Künstliche Intelligenz
    Instructor: Philipp Terhörst
  4. Maschinelles Lernen und Künstliche Intelligenz (Übung)
    Instructor: Philipp Terhörst

SoSe 2026

We provide supervision for final theses on topics related to our research topics for both bachelor’s and master’s theses. We typically present potential topics during the initial meeting. However, you are also welcome to propose your own research questions.

Bachelor’s Theses – The assignment of bachelor’s theses in Faculty 03 is handled centrally through the faculty’s ranking process. Please note the deadlines and registration procedures published there.

Master’s Theses – If you are interested in writing a master’s thesis, please contact the employee whose topic interests you, or the professor directly. During our initial meeting, we will introduce you to current topics.

Prerequisites – To write a final thesis with us, prior knowledge in the field of machine learning is required. For most topics, programming skills in Python are also expected. Topics from the lab courses can be pursued further by arrangement.