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Hyperspectral and Multispectral Image Analysis
International School of Chemometrics - 2023
ISC - 2023
José Manuel Amigo Rubio
Research topics: Data Mining, Machine Learning in Chemical data (a.k.a. CHEMOMETRICS)
His Current research interests include hyperspectral, multispectral and digital image analysis applied in many different fields (Food Sciences, Environmental Modeling, Forensic Sciences, Pharmaceutical Production, etc.) and teaching chemometrics. He has authored more than 170 publications (120+ peer-reviewed papers, books, book chapters, proceedings, etc.) and given more than 60 conferences at international meetings and given more than 30 courses world-wide. He has supervised or is currently supervising several Masters, Post Docs and PhD students and he is an editorial board member of four scientific journals within chemometrics and analytical chemistry. 2014 - Chemometrics and Intelligent Laboratory Systems Award for his achievements in the field of Chemometrics. 2019 - Thomas Hirschfeld Award for his achievements in the field of Near Infrared Technology.
Rasmus Bro (born 1965) studied mathematics and analytical chemistry at the DTU and received his M.Sc. in 1994. In 1998 he obtained his Ph.D. (Cum Laude) in multiway analysis from the University of Amsterdam. Since 1994 he has been employed at the Department of Food Science, Quality and Technology of the University of Copenhagen, and in 2002 he was appointed Full Professor of chemometrics. He has had several stays abroad at research institutions. His current research interests include chemometrics, multivariate calibration, multiway analysis, exploratory analysis, experimental design, numerical analysis, blind source separation, curve resolution, MATLAB programming, and constrained regression. In 2000 he received the third Elsevier Chemometrics Award for noteworthy accomplishments in the field of chemometrics by younger scientists, and in 2004 he received the Eastern Analytical Symposium Award for Achievements in Chemometrics. He has authored more than 200 peer-reviewed scientific papers, several books on chemometrics, and more than 20 proceedings, book contributions, reviews, and patents.
Graduated in Environmental Sciences in 2002 and since then he has been working in chemometrics and chemical modelling. In 2007 he discussed his PhD thesis (Multivariate analysis applied to food science) and he is now professor of analytical chemistry, chemometrics and chemoinformatics at the Milano Chemometrics and QSAR Research Group (University of Milano – Bicocca, Italy). He has 15 years experience in multivariate analysis applied to both analytical and QSAR (Quantitative Structure Activity Relationships) data; he also likes to code MATLAB toolboxes for the calculation of multivariate models and share them publicly. You can find some of them in the Milano Chemometrics and QSAR Research Group website: http://michem.disat.unimib.it/chm/
Federico Marini received his MSc from Sapienza University of Rome in 2000 where he also completed his PhD in 2004, and he is currently associate professor of Analytical Chemistry. In 2006, he was awarded the Young Researcher Prize from Italian Chemical Society and in 2012 he won the Chemometrics and Intelligent Laboratory Systems Award. He was Marie Curie fellow at the National Institute of Chemistry (Ljubljana, Slovenia), and has been visiting researcher in various Universities and research institutes. His research activity is focused on all aspects of chemometrics, ranging from the application of existing methods to real world problems in different fields to the design and development of novel algorithms. He is author of more than 200 publications (papers in international journals and book chapters), and he edited and coauthored the book Chemometrics in food chemistry (Elsevier). He is the past-coordinator of the Chemometric group of the Italian Chemical Society, and currently the vice-president of Italian NIR society and the coordinator of the Chemometric study group of EUCheMS.
Morten A. Rasmussen
I obtained a Msc in Food Science and Technology in 2007 and a PhD in Chemometrics in 2012 from University of Copenhagen. My focus is to brigde statistics with applied food- and Health science, and I have visited statistics departments at Stanford (2010) and Harvard (2015) to complement my profile in this regard. My research focuses on development and application of mathematical and statistical models for integration of high dimensional heterogeneous data characterizing a biological system, especially with focus on untargeted metabolomics and microbiome within food and Health. The development of high throughput analytical platforms have seeded a need for data analytical tools for integration of these heterogeneous data. Such tools should produce reliable and interpretable models. In my research we have developed tools for integration of multiple data sources, especially focusing on parameter sparsity yielding models that are often more robust but merely easier to interpret. We have done this for longitudinal sensitization patterns in childhood, immunological profiles related to asthma, plasma-metabolomics for prediction of breast cancer several years ahead, longitudinal progression of newly diagnosed type I diabetes in children and the early life microbiome in relation to development of chronic childhood disorders.
Anna de Juan
Anna de Juan is an associate professor at the faculty of Chemistry of the Universitat de Barcelona. Her research focuses on the theoretical development of Multivariate Curve Resolution (MCR) and application to process analysis, hyperspectral image analysis and general analytical and bioanalytical problems. She has published more than 140 works (h index = 41, WoS) and has given more than 200 presentations in international conferences. She received the 4th Chemometrics Elsevier Award in 2004 and the Kowalski Prize from J. of Chemometrics to the best-applied paper in 2009. She serves in the Editorial Advisory Board of Chemometrics and Intelligent Laboratory Systems since 2002 and of Analytica Chimica Acta since 2006. Her teaching activity covers undergraduate and graduate topics related to Chemometrics and Analytical Chemistry and she has been invited Prof. for short stages at U. de Lille (France), University of Dalhousie (Canada), Università di Modena e Reggio Emilia (Italy), Institute of Advanced Studies in Basic Sciences IABS, Zanjan (Iran), Universidad de Santa Fe (Argentina) and Universidad Pontificia de Valparaíso (Chile).
Department of Pharmacology and Toxicology, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health Medicine and Life Sciences (FHML), Maastricht University, The Netherlands.
Agnieszka (Agi) Smolinska studied Chemistry at Silesian University in Katowice, Poland. In 2008 she moved to the Netherlands to perform her doctoral study at Radboud University in Nijmegen (The Netherlands) in the group of Prof. Buydens. During her PhD she worked in metabolomics. She combined Nuclear Magnetic Resonance Spectroscopy and Gas Chromatography-Mass Spectrometry with advanced machine learning techniques for biomarker discovery of neurological disorders, mainly Multiple Sclerosis. She obtained her PhD degree in 2012. After her PhD, she has been working as postdoc at Maastricht University, The Netherlands and Dartmouth University in USA. Her current research group (at Maastricht University, The Netherlands) is focused on the multiple applications (for lung diseases, chronic liver diseases, inflammatory bowel disease, cancer, and irritable bowel syndrome) of volatile metabolites in different biofluids and finding their relation to the gut microbiome using chemometrics, machine learning/multivariate statistics. Since 2020 she has also been working at Owlstone Medical, Cambridge, UK, for further development of exhaled breath research for medical applications. She is a membre of metabolomics society, international association of breath research and tresurer of Ducth chemoemtrics society. She is in the editorial board of Scientific Reports, Journal of Breath Research, Metabolites and Analytical Science Advances. She was granted with various international and national awards and grants (best PhD thesis, metabolomics young scientist, Veni NWO, Niels Stensen fellowship, Transcan-2, Nutrim seeding grant), has supervised 5 doctoral theses and has authored 55 publications.
Born in 1976 in no more existing country, USSR. Defended PhD in Physics and Mathematics in 2001 and right after that he took a break and was not active as a researcher for several years. In 2004 started rebooting his carrier and decided to change research interests towards Chemometrics. He started from the scratch in 2007 as assistant professor at AAU and has been gradually developing his career as chemometrician since then.
In his spare time Sergey works on various pet-projects. Thus, from 2013 he has been developing R package “mdatools” for multivariate data analysis and preprocessing with one-two major releases every year. The results of these activities can be found at https://mda.tools and hist GitHub https://github.com/svkucheryavski
Anders Krogh Mortensen
Anders Krogh Mortensen (AKM) recieved his MSc (Information Technology) from Dept. of Engineering, Aarhus University in 2014, and completed his PhD in 2018 at Dept. of Agroecology, Aarhus University. He is currently in his last year of his 4 year postdoc at Dept. of Agroecology, Aarhus University, where his research areas are computer vision and deep learning in an agricultural setting. His work includes extracting information and building classification and regression models collected from RGB-, multispectral-, stereovision-cameras and LiDARs mounted on unmanned aerial vehicles, field robots and tractors in the field as well as multi- and hyperspectral cameras in the laboratory. This includes writing software to integrate the sensors, extracting features from the sensor data, and calibrating/training models using both hand-crafted features and raw data in data-driven approaches (deep learning). Much of this work is performed in the free and open-source programming language Python. AKM is also a proponent for open-source and therefore shares as much of his work as possible on his Github page: https://github.com/anderskm
Jesper Løve Hinrich
Jesper Løve Hinrich received the M.Sc. (2016) and Ph.D. (2020) degrees in applied mathematics from the Technical University of Denmark. He is currently a Postdoc at the Department for Food Science, University of Copenhagen, Denmark. His research interests includes multi-way modelling, Bayesian inference, statistical machine learning, and deep learning. He focuses on challenges arising in the Life Sciences, in particular (gas) chromatography, spectroscopy, neuroimaging, and metabolomics.