I am a biologist with a strong interest in how complex biological systems can be measured and understood in practice. During my Master’s in Molecular Bioscience in Heidelberg, I focused on emerging single-cell approaches, a field that continues to shape how I think about biological variability and function. I am currently pursuing my PhD at the Berlin Institute of Health and Charité University Hospital in Berlin. My research focuses on advancing single-cell technologies for applications in personalized medicine, with an emphasis on translating methodological innovation into insights that are relevant for clinical decision-making.
As part of my doctoral research, I worked with an interdisciplinary team of scientists, clinicians, and bioinformaticians to develop a proprietary technology for mapping cell-cell interactions. This platform enables the characterization of mechanisms of action for bispecific antibodies and cellular therapies, and supports biomarker discovery as well as the development of companion diagnostics in areas such as oncology, autoimmune disease, and aging. Beyond specific technologies, my broader interest lies in understanding where current analytical approaches fall short. Many biological and therapeutic processes are driven by interactions rather than isolated cellular states, yet these interactions are often difficult to capture in a systematic and scalable way. Bridging this gap between biological complexity and practical measurement is a recurring theme in my work.
In my blog I specifically explore these questions. The focus is on cell-cell interactions, single-cell and interaction-aware technologies, and their implications for immunology, cell therapy, and translational research. Rather than presenting finished answers, the goal is to think openly about emerging methods, limitations, and design choices that shape how data is generated and applied in translational medicine.
In my blog I specifically explore these questions. The focus is on cell-cell interactions, single-cell and interaction-aware technologies, and their implications for immunology, cell therapy, and translational research. Rather than presenting finished answers, the goal is to think openly about emerging methods, limitations, and design choices that shape how data is generated and applied in translational medicine.