Dr. Susanne Neufang | Medical Data Science | Best Researcher Award
Principal Investigator | Insitute of Biomedical Informatics, University of Cologne | Germany
Susanne Neufang is a data scientist and neuroscientist with extensive expertise in developmental cognitive neuroscience, neuroimaging, psychiatry, and medical data science, combining her background in psychology, brain development, and computational approaches to explore normal and pathological mechanisms of executive functions, attention, and psychiatric disorders, and more recently advancing into biomedical informatics and artificial intelligence with a strong focus on fairness, explainability, and deep learning for clinical applications, contributing to high-impact international collaborations such as PRONIA and ENIGMA and producing more than fifty publications, numerous grants, and recognition through prestigious awards, while also developing and leading interdisciplinary research groups bridging neuroscience and data science to translate neurobiological findings into innovative diagnostic and therapeutic tools, thereby positioning herself at the interface of neuroscience, medicine, and machine learning, with a research career that spans multiple universities and international institutions and continues to evolve within biomedical informatics and applied AI.
Profile
Orcid
Education
Susanne Neufang pursued undergraduate studies in psychology at the Technical University Berlin, enriched her academic foundation with an exchange at Universidad Complutense de Madrid, and completed graduate studies at the University of Bonn where she earned her M.A. in psychology with highest distinction, followed by doctoral training at Ruhr-University Bochum where she investigated gender-specific development of brain anatomy and attention functions and was awarded a Ph.D. with magna cum laude, she then advanced to habilitation at Julius-Maximilians-University Würzburg with a thesis on the normal and pathological development of executive functions across the lifespan, earning the title Privatdozentin, and later complemented her neuroscience expertise with advanced training in data science at Sorbonne Université in collaboration with Datascientest, where she acquired modern analytical and machine learning skills including deep learning, explainable AI, fairness in AI, and multimodal data analysis, building a unique interdisciplinary education profile combining psychology, neuroscience, medicine, and data science.
Professional Experience
Her professional career spans diverse roles including graduate and doctoral researcher at the Research Center Jülich and RWTH Aachen, international research training at the Sackler Institute New York, and postdoctoral appointments at Charité Berlin and Technical University Munich where she worked on developmental cognitive neuroscience and neuroimaging, before leading research groups at Würzburg and Düsseldorf focusing on developmental neuroimaging, biological mechanisms of psychiatric transitions, and neurodiagnostics, in which she combined advanced MRI and genetic data to study psychiatric disorders, while contributing to European research consortia and clinical trials, she later transitioned to the University of Cologne as a data scientist at the Institute for Biomedical Informatics, where she applies modern machine learning techniques to multimodal biomedical data, developing explainable, fair, and clinically robust AI models for psychiatry and cognitive neuroscience, thus integrating her long-standing expertise in brain research with cutting-edge data science and artificial intelligence applications for medical research.
Awards and Honors
Throughout her career Susanne Neufang has been recognized with multiple awards and scholarships reflecting both academic excellence and innovative research contributions, including a European mobility scholarship supporting her academic year in Madrid, a DAAD fellowship enabling her research at the Sackler Institute in New York, and a research fellowship from the Parmenides Foundation in Germany supporting her postdoctoral work, her scientific achievements were further honored with the August-Homburger Award for Young Scientists acknowledging her contributions to developmental neuroscience and psychiatry, in addition she secured multiple competitive national and international research grants from organizations such as the DFG, IZKF, and collaborative research centers, supporting high-impact projects on anxiety, depression, schizophrenia, genetics, and neuroimaging, her ability to attract funding and recognition underlines her standing as an innovative researcher at the crossroads of neuroscience and data science, with a strong track record of excellence and international scientific impact.
Research Focus
Her research centers on understanding the neurobiological and computational mechanisms underlying normal and pathological brain development, executive functions, attention, and psychiatric disorders such as ADHD, anxiety, depression, schizophrenia, and borderline personality disorder, integrating multimodal data sources including MRI, clinical, genetic, and socio-epidemiological datasets, she applies advanced machine learning, deep learning, and explainable AI methods to identify biomarkers, develop predictive models, and design fair and interpretable diagnostic tools for precision psychiatry, with emphasis on gender fairness, bias reduction, and robust clinical translation, her focus spans from fundamental cognitive neuroscience to applied biomedical informatics, linking brain connectivity, genetics, and behavior with computational approaches, and contributing to large-scale collaborations like PRONIA and ENIGMA to achieve reproducible and generalizable findings, by bridging neuroscience and artificial intelligence her work aims to advance early detection, prognosis, and individualized treatment strategies for mental health disorders, fostering integration of neuroimaging, genomics, and clinical data into next-generation medical AI.
Publication
Title: EMORL: Ensemble Multi-Objective Reinforcement Learning for Efficient and Flexible LLM Fine-Tuning
Year: 2025
Title: Theta burst stimulation add on to dialectical behavioral therapy in borderline-personality-disorder: methods and design of a randomized, single-blind, placebo-controlled pilot trial
Year: 2024
Title: Serotonergic modulation of normal and abnormal brain dynamics: The genetic influence of the TPH2 G-703T genotype and DNA methylation on wavelet variance in children and adolescents with and without ADHD
Year: 2023
Title: Brain-Based Classification of Youth with Anxiety Disorders: an ENIGMA-ANXIETY Transdiagnostic Examination using Machine Learning
Year: 2022
Title: Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages
Year: 2022
Conclusion
Susanne Neufang is highly suitable for recognition in research awards. Her rare combination of neuroscience expertise and advanced AI skills positions her at the forefront of precision psychiatry and biomedical data science. With continued emphasis on translational application and academic visibility, she will likely make even greater contributions to the field. Her career trajectory, international collaborations, and innovative focus make her a strong and deserving candidate for prestigious research awards.