Chemometrics, Machine Learning, Big Data analytics
The main research areas are related to development and application of multivariate data analysis tools, including novel algorithms and codes, in the context of data driven discovery. The use of chemometrics and machine learning tool to extract relevant information from instrumental data ranging from hyphenated analytical techniques to imaging, find broad applications in several fields, such as material design and characterization, multivariate statistical process monitoring and control (MSPC), food authentication, environmental and biomedical research, etc..
Research topics:
Big Data Analytics for Industry 4.0 (LV based MSPC, scalability of multivariate latent variables model to Big Data, PAT, predictive analytics)
Development of Multivariate and Multiway Calibration, Classification and pattern recognition tools
Experimental Design (DoE) methodology for formulation, sampling, sample preparation and instrumental settings
Development of Image analysis and signals processing algorithms (chemometrics and machine learning)
Integration of DeepL and chemometrics
Improving state-of the art hyperspectral imaging to resolve complex chemical samples.
Implementation of Multiblock and Data Fusion methodologies to integrate information acquired from different sensors and analytical techniques
Researchers: Marina Cocchi, Caterina Durante
