Leonard Schmiester

Leonard Schmiester is a Postdoctoral Researcher at the Oslo Centre for Biostatistics and Epidemiology (OCBE), University of Oslo, Norway, since 2021. His research focuses on computational oncology and systems biology, developing dynamical models to predict treatment outcomes in breast cancer while advancing parameter estimation methodologies for biological systems.

His academic background includes:

• PhD candidate (2016-2021) at Helmholtz Centre for Environmental Research and Technical University of Munich
• M.Sc. in Industrial Mathematics (2012-2016) from University of Hamburg
• B.Sc. in Mathematics (2008-2012) from University of Hamburg

Dr. Schmiester's research integrates mathematical modeling with clinical oncology to personalize cancer treatment. He specializes in simulating tumor evolution under therapeutic pressure, particularly for estrogen receptor-positive breast cancer subtypes. His work combines dynamical systems theory with high-throughput data to decode mechanisms of drug resistance in Luminal B breast cancer, focusing on endocrine therapy and CDK4/6 inhibitor combinations. This approach enables computational prediction of patient-specific treatment responses, contributing to precision oncology frameworks that optimize therapeutic sequencing.

Analysis of his publication record reveals two dominant research streams: clinical applications in breast cancer evolution (60% of recent work) and computational methodology development (40%). The oncology-focused publications examine immune-malignant cell co-evolution during aromatase inhibitor therapy, while methodological papers introduce innovations like pyPESTO for ODE parameter estimation and mini-batch optimization for large-scale datasets. Both streams converge on translating mathematical frameworks into clinically actionable insights for personalized cancer medicine.

Dr. Schmiester actively contributes to the Norwegian Centre for Knowledge-driven Machine Learning and RESCUER project, applying stochastic modeling to biomedical challenges. As part of the Stochastic Models and Inference research group at OCBE, he develops statistical frameworks for interpreting heterogeneous biological data, with current work extending computational oncology approaches to machine learning applications in disease progression modeling.