Research leader, Idletechs AS, BIC, 7034 NTNU Trondheim, Norway
harald.martens@idletechs.com
Idletechs
External professor, Dept. Engineering Cybernetics,
NTNU, 7034 Trondheim, Norway
harald.martens@ntnu.no
Academic degrees
MSc in Industrial Biochemistry (1971) and Dr.Techn in Biochemometrics (1985) from NTNU.
Previous academic positions
- Guest researcher Makerere U, Uganda (3 months 1972)
- Kyoto University, Japan (1 year 1973-74)
- Lund University, Sweden (1 year 1975-76)
- UC Davies, California, USA (1 year 1988-98)
- Guest professor, Biocentrum DTU, Denmark (1997-99)
- Adjunct professor, U-LIFE, Copenhagen (1999-2003)
- Professor II, Institute physical chemistry, NTNU (1998-2002)
- Professor II, IKBM, NMBU, Ås (2003-05)
- Professor II, IMT, NMBU, Ås (2005-08)
- Researcher, NOFIMA Ås (1973-86, 2003-13)
Previous non-academic positions
- Research scientist, Norwegian Computing Center, Oslo (industrial chemometrics)
- Consultant to KES Analysis, New York USA (non-invasive NIR analysis of blood glucose) 1988-89
- Consultant to Guided Wave Inc., El Dorado Hills CA USA (industrial fibre- optic NIR analysis of hydrocarbons) 1989
- Consultant to DuPont, Delaware (process chemometrics) 1989-92
- Research leader, International Digital Technologies, Munich Germany (model based video compression) 1995-97
Scientific achievements
- Co-developer of new statistical methods (PLS regression MSC/EMSC/OEMSC data preprocessing, IDLE dual-domain model-based image compression, several recent high-dimensional dynametrics modelling methods)
- Founder and “grandfather” of The Unscrambler do-it-yourself data analysis software (CAMO)
- 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
- Fellow of Near Infrared Spectroscopy, by The International Council for Near Infrared Spectroscopy October 2015
- Founder of Idletechs AS 2015 to speed up the development and understandable use of continuous high-dimensional data streams in medicine, environmental monitoring and industry, keeping people in the Big Data loop.
Publications statistics
A total of research publications in international peer review journals published from 1979 to 2015: around 250. Cited over 16 000 times, h-index 50 (Google Scholar).
Selected research papers, book chapters, review articles 2005-2016
Books:
- Multivariate Calibration (H.Martens & T. Næs) 1989 J.Wiley & Sons Ltd Chichester UK (>6500 citations)
- Multivariate Analysis of Quality: An Introduction (H.Martens & M.Martens) 2001 J.Wiley & Sons Ltd Chichester UK (>750 citations)
Reseach papers:
- Martens, H. (2011) The informative converse paradox: Windows into the unknown. Chemometrics and Intelligent Lab.Sys.107 124–138. http://www.sciencedirect.com/science/article/pii/S0169743911000487.
- Hasani S, Martens H, Quannari EM, Hanafi M and Kohler A (2012) Model validation and error estimation in multi-block partial least squares regression. Chemometrics and Intelligent Laboratory Systems, Volume 117, Pages 1-250; http://dx.doi.org/10.1016/j.chemolab.2011.06.001
- Hassani S, Martens H, Qannari, El Mostafa and Kohler, Achim (2012) Degrees of freedom estimation in Principal Component Analysis and Consensus Principal Component Analysis. Chemometrics and Intelligent Laboratory Systems 118, 15 pp 246–259
- Isaeva J, Sæbø S, Wyller JA, Wolkenhauer O and Martens H. (2012) Nonlinear modelling of curvature by bi-linear metamodelling. Chemometrics and Intelligent Laboratory Systems 117, 1 August 2012, pp 2–12 http://www.sciencedirect.com/science/article/pii/S0169743911000852
- Nhek S, Mosleth E.F., Høy M., Griessl M., Tessema B., Indahl U., Martens H. (2012) Nonlinear visualisation and pixel-based alignment of 2D electrophoresis images. Chemometrics and Intelligent Laboratory Systems. 118, 15 pp 97-108. http://dx.doi.org/10.1016/j.chemolab.2013.01.008
- Tøndel K, Indahl UG, Gjuvsland AB, Omholt SW, Martens H. (2012): Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models. BMC Syst Biol.; 6:88. doi: 10.1186/1752-0509-6-88
- Isaeva J, Sæbø S, Wyller JA, Nhek S and Martens H. (2012) Fast and comprehensive estimation and fitting of complex mathematical models to massive amounts of empirical data. Chemometrics and Intelligent Laboratory Systems 117, 1 pp 13–21. http://www.sciencedirect.com/science/article/pii/S0169743911000840
- Isaeva, J., Martens, M., Sæbø, S., Wyller, J. A. & Martens, H. (2012). The modelome of line curvature: Many nonlinear models approximated by a single bi-linear metamodel with verbal profiling. Physica D: Nonlinear Phenomena 241, 877–889
- Liland KI H, Høy M, Martens H, Sæbø S (2013): Distribution based truncation for variable selection in subspace methods for multivariate regression. Chemometrics and Intelligent Laboratory Systems, 122, 15, Pages 103–111. http://dx.doi.org/10.1016/j.chemolab.2013.01.008
- Martens, H, Tøndel K, Tafintseva V, Kohler A, Plahte E, Vik JO, . Gjuvsland AB, . Omholt SW (2013) PLS-Based Multivariate Metamodeling of Dynamic Systems. New Perspectives in Partial Least Squares and Related Methods . Springer Proceedings in Mathematics & Statistics 56, pp 3-30. DOI 10.1007/978-1-4614-8283-3_1
- Kristin Tøndel and Harald Martens (2014) Analyzing complex mathematical model behavior by PLSR-based multivariate metamodeling. WIREs Computational Statistics, Volume 6, Issue 6, pages 440–475, November/December 2014. DOI: 10.1002/wics.1325
- Tafintseva Valeria, Tøndel Kristin, Ponosov Arcady, Martens Harald (2014) Global structure of sloppiness in a nonlinear model. J. Chemometrics, Volume 28, Issue 8, pages 645–655, DOI: 10.1002/cem.2651
- Wu Tim, Martens Harald, Hunter Peter and Mithraratne Kumar (2014) Emulating facial biomechanics using multivariate partial least squares surrogate models. Int. J. Numer. Meth. Biomed. Eng. (2014) Nov;30(11):1103-20. Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/cnm.2646
- Voit EO, Martens HA, Omholt SW (2015) 150 Years of the Mass Action Law. PLoS Comput Biol 11(1): e1004012. doi:10.1371/journal.pcbi.1004012
- Kohler A, Böcker U, Shapaval V, Forsmark A, Andersson M, et al. (2015) High-Throughput Biochemical Fingerprinting of Saccharomyces cerevisiae by Fourier Transform Infrared Spectroscopy. PLoS ONE 10(2): e0118052. doi:10.1371/journal.pone.0118052
- Martens H (2015) Quantitative Big Data: Where Chemometrics can contribute, J.Chemometrics 29 (11) 563–581 http://onlinelibrary.wiley.com/doi/10.1002/cem.2740/epdf