You can't ML your way out of bad data, no matter how many parameters you add.

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 methodologies for personalized psychiatry. Marc's research focuses on characterizing the constraints and limitations of these methods to assess if and under which conditions they offer potential for clinical translation.

You can't ML your way out of bad data, no matter how many parameters you add.

Rane, R.P., Kim, J., Umesha, A., Stark, D., Schulz, MA., Ritter, K.

DeepRepViz: Identifying Potential Confounders in Deep Learning Model Predictions

Schulz, M.A., Albrecht, J.P., Yilmaz, A., Koch, A., Kainmüller, D., Leser, U. & Ritter, K.

TLIMB-a transfer learning framework for image analysis of the brain

Schulz, M.A., Bzdok, D., Haufe, S., Haynes, J.D., Ritter, K.

Performance reserves in brain-imaging-based phenotype prediction

Schulz, M.A., Hetzer, S., Eitel, F., Asseyer, S., Meyer-Arndt, L., Schmitz-Hübsch, T., et al

Data augmentation via partial nonlinear registration for brain-age prediction

Schulz, M. A., Baier, S., Timmermann, B., Bzdok, D., & Witt, K.

A cognitive fingerprint in human random number generation

Chapman-Rounds, M., Bhatt, U., Pazos, E., Schulz, M. A., & Georgatzis, K.

FIMAP: Feature Importance by Minimal Adversarial Perturbation

Eitel, F., Schulz, M. A., Seiler, M., Walter, H., & Ritter, K.

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

Schulz, M. A., Yeo, B. T., Vogelstein, J. T., Mourao-Miranada, J., Kather, J. N., Kording, K., ... & Bzdok, D.

Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets

Schulz, M. A., Chapman-Rounds, M., Verma, M., Bzdok, D., & Georgatzis, K.