Björn holds a doctorate in mathematics from the Otto-von-Guericke University Magdeburg. He has helped develop drugs in several disease areas at Novartis for 20 years. Björn is one on the authors of a collection of case studies on applied flexible Bayesian modelling in drug development with brms. He has worked on exploring the opportunities for machine learning in clinical development.

Thomas Jemielita is a Principal Scientist in Oncology Statistics, BARDS. Since joining Merck in 2017, his evolving job roles have spanned across various areas, including statistical support for early to late phase studies, biomarker studies, competitive intelligence, strategic initiatives, and real-world evidence studies. He has been actively involved in statistical research and has authored/co-authored over 20 scientific publications in peer-reviewed statistical and clinical journals, along with currently being a member of the ASA BIOP RWE Scientific Working Group for Rare Diseases. His currently research interests include causal inference, machine learning, and software development. Prior to joining Merck, Thomas received his PhD in biostatistics from the University of Pennsylvania in 2017.

Chris Harbron is an Expert Statistician leading capabilities in Advanced Analytics within the Data Sciences function at Roche. Through a variety of roles within the pharmaceutical industry Chris has worked in all stages of the drug development pipeline from drug discovery to early and late development. Chris has published and presented widely both within the statistical and the broader scientific literature.