Schwarz, Friedrich

• since 2019 Medical Doctoral Thesis, Institute for Neuropathology, University Medical Center Göttingen (Supervisors: C Stadelmann, T Moser)
• 2022–2026 MSc Applied Data Science – Computational Neuroscience, Georg-August-University Göttingen
• 2017–2023 BSc Applied Computer Science – Computational Neuroscience, Georg-August-University Göttingen
• 2016–2026 MD Human Medicine, University Medical Center Göttingen (expected 06/2026)

Major Research Interests

My research spans from wet-lab electrophysiology and complex cell culture to large-scale high-performance computing (HPC) and theoretical modelling. This multidisciplinary background allows me to bridge the gap between experimental constraints and computational analysis, converging on two main areas:

Neuronal Data Science

I develop scalable, automated infrastructures for terabyte-scale electrophysiology, including chronic optogenetic stimulation of neuronal cultures with simultaneous multi-electrode readout. My focus is on translating these large-scale input–output recordings into mechanistic insight through optimized workflow orchestration and causal analysis of neural dynamics.
In collaboration with: Dr. Andreas Neef (CIDBN), Prof. Fred Wolf (MPI-DS, CIDBN), Prof. Christine Stadelmann (UMG), Prof. Philipp Wieder (GWDG) and Prof. Kerstin Schmidt (UFRN Natal, Brazil), Cyprian Adler (CIDBN, UMG).

Clinical Data Science and Medical AI

I apply and develop machine learning, survival analysis, and causal modelling approaches, primarily applied to clinical oncology data, grounded in clinical and experimental realities, to generate robust, reproducible, and translatable results. Recently, we published an adverse-event prediction framework for chemotherapy-based stem-cell mobilization in multiple myeloma (npj Digital Medicine). Currently, I focus increasingly on safety frameworks for generative AI in clinical settings and agentic AI for interpretable registry data analysis.
In collaboration with: Dr. Enver Aydilek (University Hospital Bielefeld, UMG), Dr. Nils Brökers (UMG) and Prof. Georg Heß (University Medical Center Mainz, European MCL Registry).

Selected Recent Publications and Presentations


• Schwarz F, Levien L, Maulhardt M, Wulf G, Brökers N, & Aydilek E (2026). Predicting adverse events for risk stratification of chemotherapy based stem cell mobilization in multiple myeloma. npj Digital Medicine, doi.org/10.1038/s41746-026-02394-y.


• Schmidt KE, Domingues Fonseca A, Schwarz F, & Neuenschwander S (2025). Did the mechanisms for orientation selectivity evolve along the same lines in eutherians and metatherians? Journal of Neurophysiology, doi.org/10.1152/jn.00192.2025.


• Poster, Bernstein Conference 2025: Schwarz, F., Bandow, B., Neef, A., & Wolf, F. (2025). Combining an electronic lab notebook with an automated workflow manager: An integrated approach to electrophysiology data management, doi.org/10.12751/NNCN.BC2025.043


• Poster, Bernstein Conference 2025: Adler, C., Schwarz, F., & Neef, A. (2025). Interfacing neuronal cultures cell-by-cell, hour-by-hour, day-by-day, doi.org/10.12751/NNCN.BC2025.197


• ORCID: 0009-0001-1167-8365 GitHub: fschwar4 | goecidbn




From Wet-Lab to Bedside: Experimental Neuroscience (left):Investigation of neuronal dynamics combining multi-electrode arrays (MEA) with optogenetics. The workflow spans from hardware integration and raw signal acquisition to spike sorting and population dynamics analysis.
Research Infrastructure (middle): Development of scalable data ecosystems. This architecture integrates Electronic Lab Notebooks (LabID) with high-performance computing (HPC) and automated workflow orchestration (Apache Airflow) to ensure reproducible, terabyte-scale analysis.
Clinical AI & Decision Support (right): Application of machine learning and survival analysis to clinical oncology. Shown are treatment pathways and risk stratification models for predicting adverse events and hospitalisation requirements in multiple myeloma patients (Schwarz et al. 2026, npj Digital Medicine).