The rapid advancement of Generative Artificial Intelligence (GenAI) has unlocked new possibilities for modelling and understanding complex biomedical data. Generative models, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models, have emerged as powerful tools for tasks such as molecule generation, medical image synthesis, and biological sequence design.

The seminar will begin with a series of introductory lectures (around 4 or 5) covering the fundamentals of generative modelling and their applications in biomedical contexts. Both foundational and state-of-the-art approaches will be explored, along with their respective use cases and limitations.

These lectures will be followed by two dedicated sessions on how to write technical reports and how to deliver effective presentations.

Student seminar presentations will then take place, conducted in small groups of 1-2 students.

The course will follow a seminar+tutorial format, and as such students will:

1. Present a chosen paper on a generative model and its biomedical application;
2. Deliver a final report of around 10 pages;
3. Weekly submit a short summary (around 500 words) of the presentation held during that week.

The course will be entirely held in English.

• Prof. Dr. Alexander Schönhuth: aschoen@cebitec.uni-bielefeld.de
• Luna Pianesi: lpianesi@cebitec.uni-bielefeld.de

• Moodle https://moodle.uni-bielefeld.de/course/view.php?id=13394

• Individual presentations (S): 20 minutes, followed by 5 minutes of discussion
• Report (S): 12 pages long, excluding references (but title, table of content, figures, and tables included)
• Blog (S): 30-45 minutes of reading time (TBD more precisely in the next few weeks)
• Coding project (Ü): reproduce the results of the paper you chose to present

TBA
If there is any problem with accessing a paper, write an email to Luna Pianesi.