Biography
Susanne Neufang is a trained Psychologist, she started her neuroscientific career in the Institute of Neuroscience and Medicine, Research Centre Juelich with her master thesis project, followed by her dissertation on imaging genetics in children and adolescents with and without Attention Deficit/Hyperactivity Disorder. She made her Post-Doc at the Dept. of Diagnostical and Interventional Neuroradiology at Technical University Munich. In different steps of her career, she headed research groups (TU Munich: Developmental Cognitive Neuroscience Lab, Julius-Maximilians-University Wuerzburg: Developmental Neuroimaging Lab, Heinrich-Heine-University Duesseldorf: Research group for Neurodiagnostic) and was certified as Data Scientist by Sorbonne Université Paris. Since August 2024 she works as a researcher as the BI-K, focussing on AI model fairness (i.e. gender-bias detection and mitigation) and explainable AI (XAI) aiming to improve computer-assisted diagnostics and increase its implementation in healthcare systems.
Contact
- Email susanne.neufang@uk-koeln.de
- Office Office: Kerpener St. 62 - 50937 Köln visiting: BI-K - Geb. 705 - Zülpicher Str. 58e - 50672 Köln - 1 OG - Room 1.011
- Researchgate
- github
- publications (google scholar)
- publications (PubMed)
Academic Background
Areas of Expertise
Research Focus
- fair, de-biased clinical predictions
- Neuroscience, neural signaling, radiomics
- (molecular)genetics
Current Teachings
AI in Medicine Series
Artificial intelligence is already fundamentally changing medicine, but how do the underlying methods work, and what opportunities and challenges do they present? In this series of seminars, each session will cover a new, practical topic, including the basics of some AI methods, ethical challenges and possible solutions. The lectures, depending on the speaker, could be in German or English, are thematically linked but self-contained
Show in KLIPSWissPro - Programmierung
Die Studierenden beschäftigen sich mit einem Projekt aus dem Themenbereich der medizinischen Datenanalyse. Im Rahmen eines Einführungsseminars werden grundlegende Kenntnisse in Python erworben. Im Anschluss erhalten die Studierenden eine Aufgabenstellung, die sie innerhalb von sechs Wochen implementieren sollen. Während dieser Zeit findet ein freiwilliges wöchentliches Seminar statt, indem Probleme besprochen werden. Zum Schluss sollen die Ergebnisse in einem Vortrag vorgestellt werden.
Show in KLIPS