Teaching and Seminars

We contribute to the teaching portfolio at Friedrich Schiller University (FSU) by developing an integrated, interdisciplinary teaching program spanning cognitive neuroscience, brain imaging, and machine learning, aimed at both FSU students and national and international participants.

Course 1: Parsing Individual Differences in Brain Disorders.

This course introduces the foundations of individual-level inference in mental health, with a focus on normative modeling, and is structured into three parts. In Part I, students read a popular science book about a scientist living with bipolar disorder “An Unquiet Mind: A Memoir of Moods and Madness - Kay Redfield Jamison” and discuss lived experience, representation, and the diversity of how mental health conditions are expressed, motivating a shift beyond average-based case–control thinking. Part II introduces conceptual frameworks for individual differences, including HiTOP, RDoC, and normative modeling; through lectures and student-led paper discussions, students examine deviation mapping, heterogeneity, and the promises and limitations of these approaches. In Part III, students learn what biomarkers are and how they are constructed, develop Python scripts to derive biomarkers from functional MRI data, and estimate normative models using a GAMLSS-based toolkit developed in our lab, gaining a practical understanding of how individual-level deviation scores are computed and why they constitute a central analytic currency in modern mental health research. If students wish to receive a grade for this course, assessment will be based on active participation, a presentation, and attendance of at least four of the seminars listed below, culminating in a final presentation.

Course 2: The 50 Most Influential Ideas in Neuroscience, Psychology & Machine Learning.

This course offers an inspiring and fast-paced tour through the ideas that have shaped modern neuroscience, intentionally crossing disciplinary boundaries into psychology and machine learning. Each year, 14 influential ideas are selected from a curated pool of 50 seminal concepts, with approximately eight drawn from neuroscience, three from psychology, and three from machine learning, anchoring the course in neuroscience while highlighting the ideas that have most profoundly transformed it. The selection is randomized each year, ensuring intellectual diversity, renewal, and a sense of discovery. The course is delivered in weekly two-hour sessions held on most Thursdays during the summer term (12:15–13:45). Each lecture introduces a central idea alongside the key papers that defined its origin, impact, and influence across fields. Selected sessions include student-led presentations of seminal papers, encouraging critical evaluation and active participation. By the end of the course, students will have developed a broad, coherent overview of foundational ideas, an understanding of how they influenced one another, and a clear perspective on how neuroscience, psychology, and machine learning have jointly shaped contemporary thinking. If students wish to receive a grade for this course, assessment will be based on active participation, a presentation, and an in-depth critical evaluation of a research paper, as well as attendance at all lectures.

Semiar: Neuroscience and Machine Learning (Selective).

This seminar is designed for highly motivated students who are considering joining our lab for a Master’s thesis or a related research project. Participation is highly selective and limited to a maximum of five students, creating an intensive, interactive setting with close mentorship. Admission is based on a short letter of motivation and overall fit with the lab’s research focus. The seminar takes place Thursdays from 14:00–15:30 and features in-depth presentations by postdoctoral researchers, PhD students, and external collaborators, offering students first-hand insight into cutting-edge research in the lab. Participants are expected to engage actively in discussions and, at an appropriate stage, to present either a seminal paper or their own research idea or proposal. Students interested in participating should submit a brief letter of motivation and CV via email.