معرفی
Susanne Merz is a Researcher affiliated with the School of Science, focusing on interdisciplinary studies at the intersection of neuroscience and computational methods. Her work leverages advanced statistical and artificial intelligence techniques to investigate brain dynamics and cognitive disorders.
Her research interests include:
- Neuroscience
- Machine Learning
- Medical Imaging
- Biostatistics
- Cognitive Psychology
- Neuroinformatics
Recent publications highlight her contributions to analyzing magnetoencephalography (MEG) data for neural fingerprinting and developing AI-driven tools for dementia risk assessment in mild cognitive impairment (MCI). Collaborations span international institutions, such as the AI-Mind project funded by the EU H2020 Framework program. Her work integrates computational modeling with clinical applications, aiming to improve early detection of neurodegenerative diseases.
Scientific contributions:
- 2024: Bayesian reduced rank regression and neural fingerprints in MEG
- 2024: AI-Mind protocol for brain connectivity screening in MCI
Projects:
- AI-Mind (2021–2026), EU H2020 Framework program
Her research employs magnetoencephalography, functional connectivity, and AI to advance understanding of brain networks and their implications for cognitive health.
