If statistics is the grammar of science, we should use it to try to understand the different factors of neurological disorders.

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 University of Munich. He also studied Computational Statistics and Machine Learning at University College London and received a Master of Science degree in 2018. His research focuses on representation learning in neuroimaging using artificial neural networks to decipher diseases progression in neurological disorders.

If statistics is the grammar of science, we should use it to try to understand the different factors of neurological disorders.

Seiler, M., Ritter, K.

Pioneering new paths: the role of generative modelling in neurological disease research

Noteboom, S., Seiler, M., Chien, C., Rane, R. P., Barkhof, F., Strijbis, E.M.M,...& Ritter, K.

Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis

Chien, C., Seiler, M., Eitel, F., Schmitz-Hübsch, T., Paul, F., & Ritter, K.

Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity

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

Moritz Seiler

Pioneering new paths: the role of generative modelling in neurological disease research

Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis

Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity

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