Philipp studied physics at the University of Leipzig, Germany. He minored in chemistry and mathematics and focused on theoretical physics. He gained first research experience in nonlinear dynamics and statistical mechanics at University of Leipzig, and went on to study evolutionary game theory, evolutionary dynamics, and population genetics. Philipp did his PhD project at the Max Planck Institute for Evolutionary Biology (MPI) in Germany, and received his PhD from University of Kiel, Germany in 2011.

From 2013 to 2017, Philipp was a research fellow at Harvard T. H. Chan School of Public Health. He worked at the Dana-Farber Cancer Institute and the Program for Evolutionary Dynamics of Harvard, funded by a grant from the German Academy of Sciences Leopoldina.

From 2017 to 2021 Philipp was a member of the Department of Integrated Mathematical Oncology (IMO) at Moffitt Cancer Center, with co-affiliations in Malignant Hematology and Blood and Marrow Transplant and Cellular Immunotherapy. He is currently a Project Leader at in the Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology

Philipp's publications on google scholartwitter, github (also contributions to mathonco)




Personal Research Statement

My interests are in modeling dynamical processes in evolving cell populations, with a particular focus on genetic or complex diseases, such as cancer. I have contributed to understanding how complex cell-to-cell interactions shape the evolutionary outcomes, how diversity in hematopoietic stem cells shapes selection in peripheral blood, how cancer stem cell dynamics under therapy shape patient outcomes, and how co-evolution of cytokine producer and non-producer cells drive tumor growth. My goals are to better understand if and how external (e.g. environmental), as well as cell intrinsic (e.g. immune system) factors drive somatic evolution and selection in human tissues and tumors, and how these dynamic factors can be held accountable to quantify cancer emergence, major shifts in disease burden, and tumor progression.

My current research investigates how diverse cancer cell populations are influenced by internal (genetic or epigenetic) and external (micro-environmental) changes. As these changes shape selective pressures, I seek to quantify cancer evolutionary dynamics and cancer ecological interactions. I use mathematical, computational, and statistical modeling in combination with clinical and experimental data. For predictions, I mainly use existing and further develop new methods for statistical inference and predictive models, to facilitate the search for cancer cures and help discover novel cancer prevention strategies.