Keynote Speakers

Full Professor

Department of Chemistry, Biology and Biotechnology, University of Perugia, Italy.

In 2004, she completed her Ph.D in Chemical Science (University of Perugia) and in Organic Chemistry (University of Bordeaux). From 2008 to 2011, she worked for Multivariate Infometric Analysis (Italy), collaborating with the software company Molecular Discovery Ltd (UK) and running projects as a cheminformatician with several major pharmaceutical companies. Back to academia in 2011, she combines organic chemistry, mass spectrometry, and cheminformatics to develop and test novel computational tools for pharmaceutical applications. She also leads the design and development of the Lipostar and MARS software for untargeted lipidomics and metabolomics, in collaboration with Molecular Discovery and  MassAnalytica company. She is Working Group Leader at the “Pan-European Network in Lipidomics and EpiLipidomics”(EpilipidNET) for the group "Epilipidomics analysis and data integration strategies", which includes about 145 members from 37 countries.

Tormod is educated as statistician and has for most of his career worked in various areas within food science. The first part of his career was devoted to method development and applications in NIR spectroscopy. His interest then drifted towards sensory and consumer science, without forgetting spectroscopy. Process modelling and experimental design have also been among his interested. Tormod has published a large number of papers and books in the areas of spectroscopy, food science, sensory analysis and consumer science. He has served on several editorial boards and has also been European editor of J. Chemometrics and Associate editor of Technometrics. Tormod has received a number of international awards. He has been employed at Nofima, Norway, for most of his academic career, but has also been employed as professor in part-time positions at Univ. Oslo and Univ. Copenhagen for longer periods. For some years he was private consultant for industry in Norway and abroad. 

Dr. Stephen Master is Chief of the Division of Laboratory Medicine and holds the Michael J. Bennett Endowed Chair in Pathology at the Children’s Hospital of Philadelphia (CHOP).  He also serves as Director of the CHOP Center for Diagnostic Innovation, Medical Director of the Michael Palmieri Laboratory for Metabolic and Advanced Diagnostics, and Associate Professor of Pathology and Laboratory Medicine at the Perelman School of Medicine, University of Pennsylvania.  Dr. Master received his undergraduate degree in molecular biology from Princeton University, and his M.D. and Ph.D. from the University of Pennsylvania.  He then proceeded to a residency in Clinical Pathology at the Hospital of the University of Pennsylvania, where he also served as Chief Resident in Clinical Pathology.  In 2021-22, Dr. Master served as President of ADLM (formerly AACC).

Dr. Master’s recent professional focus has centered on the use of LC-MS/MS for biomarker discovery and measurement (both in proteomics and metabolomics) as well as on the use of bioinformatics and machine learning in laboratory medicine.

Yassene is an assistant professor at the Center for Proteomics and Metabolomics, Leiden University Medical Center, the Netherlands. He is also an adjunct professor at McGill University’s Gerald Bronfman Department of Oncology, Canada. He received his PhD from the University of Göttingen, Germany. Yassene’s recent activities focus on quantitative targeted omics and its clinical applications, omics data integration, and statistical learning. He has co-authored multiple works on precise quantitation of blood plasma proteins using mass spectrometry and heavy labeled internal standards. He is also active in perusing new approaches for omics data analysis through automation of information extraction and machine learning.

Andreas Baum is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark. He teaches applied statistics and machine learning methodologies at both the undergraduate and graduate levels. Andreas obtained his PhD in collaboration with FOSS Analytical in 2013, focusing on chemometric method development using enzymatic perturbation and tensor decompositions applied to infrared spectroscopic data.

In recent years, he has dedicated his research to exploring how latent variable space methods can enhance the analysis of process data from pharmaceutical production and other biotechnology processes. His work emphasizes the analysis of multiple time series and image data, with a specific focus on situation-dependent process optimization through representation learning techniques. His major research motivation lies in the intricate connection between traditional chemometric methods and modern deep learning and machine learning techniques.

Additionally, over the past four years, Andreas has held several Data Science Specialist roles at various departments within Novo Nordisk and Novo Nordisk Engineering, where he has been instrumental in advancing process optimization through data-driven approaches.