Author Archives: Rasmus Bro

Please join Dec 22, 15.00 (CET) when Pepe will talk about sparse models and all the pitfalls they have. See more here: https://www.linkedin.com/events/7406963924174020608/

Join us Monday Sept 29 at 15 CET for a webinar with Jesper Løve Hinrich The vast majority of tensor decomposition methods are based on least squares estimation – or equivalent maximum likelihood under a Gaussian distribution. In this presentation, I will introduce and motivate probabilistic tensor decomposition – based on Bayesian inference – andContinue reading “Webinar: Probabilistic Tensor Decomposition”

August 29, 2025, Oksana Mykhalevych (Palamarchuk) defended her thesis “Data, Models, and Meaning: Structure–Function Insight in Carrageenan via PAT”. It was a very convincing and joyful event and if you are interested in the thesis just go here (search “oksana” to find it easily)

(Join us Monday July 7 at 15 CET for a webinar Davide Ballabio and Christian Rojas on their interesting work on taste prediction. The webinar can be found at https://www.youtube.com/watch?v=ZMfVM69hxm4 Computational models that predict the taste of molecular tastants based on their chemical structure and machine learning classifiers serve as powerful tools in the advancingContinue reading “Webinar: ChemTastesDB – a curated database for the prediction of molecular taste”

Join our two courses on Design of Experiments and on basic chemometrics and machine learning.

There are many nice tools for analyzing fluorescence data. Many use the good old Nway toolbox or PLS_Toolbox/Solo. PLS_Toolbox has a very nice user-interface for handling Rayleigh, Raman and other EEM-specific artefacts. There are other more specialized tools. DOMFluor was a thing we wrote years back, but nowadays we recommend drEEM. There are tutorials onContinue reading “Analyze EEM data with PARAFAC”

April 14 at 15 CET, Manuel A. Palacios and Barry M. Wise from Eigenvector Research gave a webinar about the very intriguing Diviner. You can see the webinar here. The story: AutoML, or Automated Machine Learning, represents a paradigm where the entire pipeline of data preprocessing, variable selection, feature engineering, model selection, and hyperparameter tuningContinue reading “Semi-Automated Machine Learning for Calibration Model Development”

Monday March 24, 15.00 (CET) Joan Borràs from Kensight talked about causal models and QbD: Latent variable-based models, such as Partial Least Squares (PLS), are essential tools in the Quality by Design (QbD) paradigm, enabling the analysis of highly correlated datasets, typical of Industry 4.0, while preserving causal relationships in the reduced latent space. AlignedContinue reading “Monday Webinar: Causal latent space-based models in the Quality by Design paradigm”

Raffaele VitaleUniv. Lille, CNRS, LASIRE (UMR 8516), Laboratoire Avancé de Spectroscopie pour les Interactions, la Réactivité et l’Environnement, F-59000 Lille, France In the domain of bilinear curve resolution, the identification and extraction of essential information from sets of multivariate measurements has recently garnered significant attention primarily in the light of the fact that such anContinue reading “Monday webinar: Three-way data reduction based on essential information”

Alberto FerrerMultivariate Statistical Engineering Group (GIEM)Dpt of Applied Statistics, Operations Research and QualityUniversitat Politècnica de València In this webinar Feb 17, 15 pm CET, Alberto Ferrer will address the potential of Latent Variables-based Multivariate Statistical Models such as Partial Least Squares Regression (PLS) for facing some challenges in Industry 4.0 by exploiting its property ofContinue reading “Monday webinar: Causality in the latent space: the nice property of PLS for process optimization in digitalized Industry 4.0”