Harald Martens (1946) is Adjunct Professor at the Department of Engineering Cybernetics. He is a member of the research group for Cybernetics in Biomedicine. Martens has an MSc in industrial biochemistry and a Dr.techn in chemometrics and multivariate calibration from NTNU. He has written over two hundred research papers and several books on multivariate data modelling, which in total have been cited over 17 900 times (2017).

Present positions

• Adjunct Professor, Dept. of Engineering Cybernetics, NTNU
• Research leader in IDLETechs AS (quantitative, interpretable analysis of Big Data)

Previous academic positions

• Guest researcher Makerere U, Uganda (3 months)
• Kyoto University, Japan (1 year)
• Lund University, Sweden (1 year)
• UC Davies, California, USA (1 year)
• Guest professor, Biocentrum DTU, Denmark (5 years)
• Adjunct professor, U-LIFE, Copenhagen (10 years)
• Professor II, Institute physical chemistry, NTNU (5 years)
• Professor II, IKBM, NMBU, Ås (5 years)
• Professor II, IMT, NMBU, Ås (5 years)
• Researcher, NOFIMA Ås (1973-86, 2003-2013)

Honours

• Member of Norwegian Academy of Technical Sciences (NTVA)
• Herman Wold gold medal from Swedish Chemical Society
• Honorary member of Norwegian Chemometric Society
• Chemometrics Prize/Eastern Analytical Symposium, USA

Current teaching in «real-world» data modelling:

• TTK19 - How to discover structures and relations in complex systems?                  http://www.itk.ntnu.no/emner/fordypning/ttk19
• TK8116 - Multivariate Data and Meta Modelling: Preparing for Big Data Cybernetics. http://www.ntnu.edu/studies/courses/TK8116#tab=omEmnet

These data analysis courses are of applied, cognitive and philosophical nature, intended to motivate students for later real-world R&D. Mathematical “soft modelling”, statistical cross-validation and graphical interpretation are combined generically to study Quantitative Big Data with the eyes of physics. Students perform one complete group research project, consisting of problem formulation, experimental design, actual measurements, data preprocessing, one-block multivariate data modelling (PCA) for overview and data clean-up, renewed measurements, two-block multivariate data modelling (PLSR) for overview  and quantitative prediction, with cross-validation, graphical model interpretation and final reporting. Data tables from simple series of high-dimensional sound-spectra, or from students’ own projects, are used as a representative of the future’s multichannel measurements.

Current research activities:

• Easier use of «never-ending» megavariate data streams, e.g hyperspectral video
• New methods for model development in nonlinear dynamics
• New ways to reach the creative and the empathic student personality types wrt math and statistics, with a focus on discovery and meaning, rather than proofs and exactness