Dr. Andreas Henrici

Work at ZHAW

Focus

NMR spectroscopy, Dynamical systems, Analysis

Education and Continuing education

Continuing Education

Neural Networks and Deep Learning for Life Sciences and Health Applications
ZHAW Life Sciences and Facility Management
02 / 2019

Network

ORCID digital identifier

ORCID ID: 0009-0008-5124-604X

Projects

• Smart Acquisition for Ultra-High field NMR Spectroscopy / Project leader / ongoing
• European Conference Series on Artificial Intelligence in Industry and Finance / Project leader / completed
• Machine learning for NMR spectroscopy / Project leader / completed
• 3rd European COST Conference on Mathematics for Industry in Switzerland / Team member / completed
• 2nd European COST Conference on Mathematics for Industry in Switzerland / Team member / completed
• 1st European COST Conference on Mathematics for Industry in Switzerland / Team member / completed

Publications

Articles in scientific journal, peer-reviewed

• Fischetti, G. et al. (2025) 'A deep learning framework for multiplet splitting classification in 1H NMR', Journal of Magnetic Resonance, 373(107851). doi: 10.1016/j.jmr.2025.107851.
• Meshkian, M. et al. (2025) 'Analysis of 1D NMR spectra with 2D image processing techniques', Physica Scripta, 100(2), p. 026011. doi: 10.1088/1402-4896/ada595.
• Henrici, A. and Robbiani, M. (2024) 'Analytical models of experimental artefacts in an ill-posed nonlinear ODE system', Mathematics, 12(23), p. 3675. doi: 10.3390/math12233675.
• De Lorenzi, F. et al. (2024) 'Bayesian analysis of 1D 1H-NMR spectra', Journal of Magnetic Resonance, 364(107723). doi: 10.1016/j.jmr.2024.107723.
• Altenburger, R., Henrici, A. and Robbiani, M. (2024) 'Analytical solution of an ill-posed system of nonlinear ODE's', Communications in Nonlinear Science and Numerical Simulation, 130(107762). doi: 10.1016/j.cnsns.2023.107762.
• Schmid, N. et al. (2023) 'Deconvolution of 1D NMR spectra : a deep learning-based approach', Journal of Magnetic Resonance, 347(107357). doi: 10.1016/j.jmr.2022.107357.
• Fischetti, G. et al. (2023) 'Automatic classification of signal regions in 1H nuclear magnetic resonance spectra', Frontiers in Artificial Intelligence, 5(1116416). doi: 10.3389/frai.2022.1116416.
• Henrici, A. and Osterrieder, J. (2022) 'Editorial: Artificial intelligence in finance and industry: highlights from 6 European COST conferences', Frontiers in Artificial Intelligence, 5(1007074). doi: 10.3389/frai.2022.1007074.
• Henrici, A. (2018) 'Nekhoroshev stability for the Dirichlet Toda lattice', Symmetry, 10(10), p. 506. doi: 10.3390/sym10100506.
• Henrici, A. (2015) 'Symmetries of the periodic Toda lattice, with an application to normal forms and perturbations of the lattice with Dirichlet boundary conditions', Discrete and Continuous Dynamical Systems, Series A, 35(7), pp. 2949–2977. doi: 10.3934/dcds.2015.35.2949.

Written conference contributions, peer-reviewed

• Fischetti, G. et al. (2025) 'Fully automated analysis of photo-CIDNP NMR spectra for fast fragment screening', in 21st European Magnetic Resonance Congress (EUROMAR), Oulu, Finnland, 6-10 July 2025. ZHAW Zurich University of Applied Sciences. doi: 10.21256/zhaw-33657.
• Schmid, N. et al. (2025) 'MolDETR : next-generation analysis of molecular spectra with deep learning', in 21st European Magnetic Resonance Congress (EUROMAR), Oulu, Finnland, 6-10 July 2025. Oulu: EUROMAR. doi: 10.21256/zhaw-33659.
• Schmid, N. et al. (2024) 'Automated spin system analysis in NMR spectroscopy with SpinDETR : a deep learning approach', in Datalab-Symposium, Winterthur, Schweiz, 12. September 2024. Winterthur: ZHAW Zurich University of Applied Sciences. doi: 10.21256/zhaw-31443.
• Fischetti, G. et al. (2024) 'MuSe Net : a deep learning framework for trustworthy multiplet segmentation in 1D 1H NMR spectra', in Datalab-Symposium, Winterthur, Schweiz, 12. September 2024. Winterthur: ZHAW Zurich University of Applied Sciences. doi: 10.21256/zhaw-31444.
• Fischetti, G. et al. (2024) 'MuSe Net : a deep learning framework for trustworthy multiplet segmentation in 1D 1H NMR spectra', in 20th European Magnetic Resonance Congress (EUROMAR), Bilbao, Spain, 30 June - 4 July 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-31102.
• Schmid, N. et al. (2024) 'Automated spin system analysis in NMR spectroscopy with SpinDETR : a deep learning approach', in 20th European Magnetic Resonance Congress (EUROMAR), Bilbao, Spain, 30 June - 4 July 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-31101.
• Schmid, N. et al. (2023) 'Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning', in Prisner, T. (ed.) Euromar 2022 Abstractbook. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, p. 291. doi: 10.21256/zhaw-29510.
• Fischetti, G. et al. (2023) 'Uncertainty quantification for reliable automatic multiplet classification in 1H NMR spectra', in Prisner, T. (ed.) Euromar 2023 Programme & Abstract Book. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, p. 350. doi: 10.21256/zhaw-29538.
• Schmid, N. et al. (2023) 'Deconvolution of NMR spectra : a deep learning-based approach', in Datalab Symposium, Winterthur, Schweiz, 11. Januar 2023. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-27429.
• Fischetti, G. et al. (2022) 'A deep ensemble learning method for automatic classification of multiplets in 1D NMR spectra', in Prisner, T. (ed.) EUROMAR 2022 Abstractbook. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, p. 236. doi: 10.21256/zhaw-27328.
• Schmid, N. et al. (2022) 'Deconvolution of NMR spectra : a deep learning-based approach', in Prisner, T. (ed.) EUROMAR 2022 Abstractbook. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, p. 242. doi: 10.21256/zhaw-27336.
• Henrici, A. (2017) 'Nekhoroshev theorem for the Dirichlet Toda chain', in Symmetry 2017 - 1st International Conference on Symmetry, Barcelona, Spain, 16-18 October 2017. Winterthur: ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-5533.
• Henrici, A. and Neukom, M. (2016) 'Synchronization of van der Pol oscillators with delayed coupling', in Timmermans, H. (ed.) International Computer Music Conference Proceedings 2016. San Francisco: International Computer Music Association, pp. 213–218. doi: 10.21256/zhaw-4799.
• Henrici, A. and Neukom, M. (2016) 'Synchronization in chains of van der Pol oscillators', in Grossmann, R. and Hajdu, G. (eds) Proceedings SMC 2016, 31.8. - 3.9.2016, Hamburg, Germany. Hamburg: Hochschule für Musik und Theater, pp. 216–221. doi: 10.21256/zhaw-5503.

Other publications

• Henrici, A. et al. (eds) (2023) Artificial Intelligence in Finance and Industry : Volume II - Highlights from the 7th European Conference, 7th European COST Conference on Artificial Intelligence in Industry and Finance, Winterthur, Switzerland, 28 September 2022. Frontiers Research Foundation. Available at: https://www.frontiersin.org/research-topics/38909/.
• Henrici, A., Füchslin, R. M. and Schwendner, P. (2023) 'Editorial: Artificial Intelligence in Finance and Industry: volume II—highlights from the 7th European conference', Frontiers in Artificial Intelligence, 6(1267377). doi: 10.3389/frai.2023.1267377.
• Deflorin, P. et al. (eds) (2022) Artificial Intelligence in Finance and Industry : highlights from 6 European COST conferences, 5th European COST Conference on Artificial Intelligence in Industry and Finance, Winterthur, Switzerland (online), 3 September 2020. Frontiers Research Foundation. Available at: https://www.frontiersin.org/research-topics/18514/.

Oral conference contributions and abstracts

Henrici, A. (2018) 'Nekhoroshev theorem for the Toda lattice with Dirichlet boundary conditions', in Proceedings, Volume 2, Symmetry 2017. MDPI. doi: 10.3390/proceedings2010018.

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