Machine Learning / Translational Barriers

We explore challenges in applying machine learning to neuroscience and psychiatry, focusing on explainability, data noise, confounders, and generalizability to enhance clinical applications.

Machine Learning

Nys Tjade Siegel

Tjade is a dedicated researcher specializing in the application of advanced deep learning models to structural brain MRI data. With a robust…
Machine Learning

Prof. Dr. Kerstin Ritter

Prof. Dr. Kerstin Ritter is a Full Professor of Machine Learning for Clinical Neuroscience at the University of Tübingen and is a Director at the…
Machine Learning

Marina Lex

Marina is a Ph.D. student in the Department of Psychiatry and Psychotherapy at Charité – Universitätsmedizin Berlin and associated with the Department…
Machine Learning

Roshan Rane

Roshan’s Ph.D. research lies at the crossroads of computer science and mental health research. He analyses large population neuroscience databases…
Machine Learning

Dr. Marc-André Schulz

With a background in physics, Marc transitioned to machine learning and deep learning, specializing in the development and critical evaluation of…
Machine Learning

Moritz Seiler

Moritz Seiler holds a degree in Business Administration (B.Sc.) from the University of Bayreuth and in Statistics (B.Sc.) from the Ludwig Maximilian…
Machine Learning

Dr. Didem Stark

Didem Stark is interested in understanding bias and fairness in machine learning models used for clinical neuroimaging data. Using various fairness…