John H. Kalivas is a Professor in the Department of Chemistry at Idaho State University. He is the author or coauthor of over 130 professional papers, book chapters, and books dealing with chemometrics. He serves as Editor for the Journal of Chemometrics, Associate Editor for the journal Applied Spectroscopy, and serves on the Editorial Board of the journals Analytical Letters and Talanta. His primary expertise is analyte calibration (training) and prediction for model updating concentrating on transfer learning situations. Research focuses on methodology development using unlabeled data (transductive semi-supervised learning) and local modeling leveraging hidden physicochemical properties as information for identifying appropriate calibration samples and models for target samples of interest. A project on the horizon is using immersive analytics for virtual reality data visualization in order to select improved tuned models and samples, identify outlier (bad) data, and enhance classification.
Since 2008, Nicolas Dobigeon has been with Toulouse INP (INP-ENSEEIHT, University of Toulouse) where he is currently a Professor. He conducts his research activities within the Computer Science Research Institute of Toulouse (IRIT) and currently holds an AI Research Chair at the Artificial and Natural Intelligence Toulouse Institute (ANITI). He is a Junior Member of the Institut Universitaire de France (IUF) and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). His recent research activities have focused on machine learning and statistical signal and image processing, with a particular interest in Bayesian inverse problems and applications to remote sensing, biomedical imaging and microscopy.
Jean-Michel Marin is Professor of Statistics at University of Montpellier since 2008. His research interests include Bayesian statistics, Model choice, Approximate Bayesian Computation, Importance sampling schemes, Graphical and mixture models and its privileged application field is population genetics. He has published more than 50 publications in statistics and applied mathematics (h-index: 31). He has also published two books with Christian Robert. He has an undeniable international recognition in the field of Bayesian statistics. Beetwen 2014 and 2021, he has been the chair of the Alexander Grothendieck Montpellier Institute covering all fields of mathematics and involving around 150 researchers. Between 2018 and 2021, he has also been the president of the French Statistical Society.
Thomas Bocklitz studied physics at the university of Jena, and he received his Ph.D. in physical chemistry/chemometrics from the same university in 2011. In 2013 he became head of a junior research group “Statistical Modelling and Image Analysis” at the university of Jena. Since 2019 he is head of the research department “Photonic data science” at the Leibniz IPHT. His main research area is closely connected with the photonic data life cycle, which contains machine learning and chemometrics based modeling of photonic data. He has published more than 121 publications in peer reviewed journals and gave more than 50 invited talks on conferences. The work of Thomas Bocklitz was award with prestigious awards, like the Kaiser-Friedrich research-award in 2018 and the Bruce Kowalski award in 2015.
