Dr. Simone Ulzega

Projekte

• Feature Learning for Bayesian Inference / Teammitglied / abgeschlossen
• Maschinelles Lernen für NMR-Spektroskopie / Teammitglied / abgeschlossen
• Data mining in neurological medicine / Projektleiter:in / abgeschlossen
• Digitale Simulation zur individualisierten Fertigung von 3D Nanofaserfilter und Integration in Vollschutzanzug für Pandemiefälle / Teammitglied / abgeschlossen
• BISTOM – Bayesian Inference with Stochastic Models / Projektleiter:in / abgeschlossen
• A cloud-based IoT approach for food safety and quality prediction / Teammitglied / abgeschlossen
• Advanced Bayesian inference with stochastic hydrological models / Teammitglied / abgeschlossen

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed

• Amacker, J. et al. (2026) 'Shared local brain dynamics in pediatric and adult non-rapid eye movement parasomnias', Sleep, 49(7), p. zsag123. doi: 10.1093/sleep/zsag123.
• Penza, V. et al. (2024) 'Reconstruction of the total solar irradiance during the last millennium', The Astrophysical Journal, 976(1), p. 11. doi: 10.3847/1538-4357/ad7c49.
• Ulzega, S. and Albert, C. (2023) 'Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model', Hydrology and Earth System Sciences, 27(15), pp. 2935–2950. doi: 10.5194/hess-27-2935-2023.
• Bacci, M. et al. (2023) 'A comparison of numerical approaches for statistical inference with stochastic models', Stochastic Environmental Research and Risk Assessment. doi: 10.1007/s00477-023-02434-z.
• Albert, C. et al. (2021) 'Can stochastic resonance explain recurrence of Grand Minima?', The Astrophysical Journal Letters, 916(2), p. L9. doi: 10.3847/2041-8213/ac0fd6.
• Weyland, M. S. et al. (2020) 'Holistic view on cell survival and DNA damage : how model-based data analysis supports exploration of dynamics in biological systems', Computational and Mathematical Methods in Medicine, 2020. doi: 10.1155/2020/5972594.
• Cousin, S. et al. (2016) 'High-resolution two-field nuclear magnetic resonance spectroscopy', Physical Chemistry Chemical Physics, 18(48), pp. 33187–33194. doi: 10.1039/C6CP05422F.

Schriftliche Konferenzbeiträge, peer-reviewed

• Ulzega, S. and Albert, C. (2019) 'Bayesian inference for solar dynamo models', in 1st Swiss "Workshop on Machine Learning for Environmental and Geosciences" (MLEG2019), Dübendorf, 16-17 January 2019.
• Weyland, M. et al. (2019) 'Dynamic DNA damage and repair modelling : bridging the gap between experimental damage readout and model structure', in Cagnoni, S. et al. (eds) Artificial Life and Evolutionary Computation. Cham: Springer, pp. 127–137. doi: 10.1007/978-3-030-21733-4_10.

Weitere Publikationen

• Ulzega, S. and Albert, C. (2019) 'Bayesian parameter inference with stochastic solar dynamo models', in Platform for Advanced Scientific Computing (PASC19), Zurich, 12-14 June 2019. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-3225.
• Ulzega, S. (2018) 'Calibrating stochastic models for understanding solar activity', Transfer, 2018(1), p. 8. doi: 10.21256/zhaw-3851.
• Ulzega, S. (2017) Boosting parameter inference with stochastic models using molecular dynamics and high-performance computing.

Mündliche Konferenzbeiträge und Abstracts

• Ulzega, S., Albert, C. and Beer, J. (2023) 'Shedding light on the sun through the calibration of solar dynamo models on millennial records of solar activity', in 5th Swiss SCOSTEP Workshop, Windisch, Switzerland, 15-16 May 2023. Available at: http://scostep2023.cs.technik.fhnw.ch/pres/ulzega_scostep_2023.pdf.
• Ulzega, S. and Albert, C. (2023) 'Boosting Bayesian parameter inference of SDE models by Hamiltonian scale separation : a real-world case study in urban hydrology', in 3rd biennial meeting of the ISBA Section on Bayesian Computation (Bayes Comp), Levi, Finnland, 15-17 March 2023.
• Albert, C. and Ulzega, S. (2020) 'Stochastic resonance could explain recurrence of Grand Minima', in EGU General Assembly 2020, online, 4-8 May 2020. doi: 10.5194/egusphere-egu2020-15185.
• Albert, C., Gaia, F. and Ulzega, S. (2020) 'Can stochastic resonance explain the amplification of planetary tidal forcing?', in EGU General Assembly 2020, Online, 4-8 May 2020. Available at: https://presentations.copernicus.org/EGU2020/EGU2020-15185_presentation.pdf.
• Ulzega, S. and Albert, C. (2019) 'Bayesian inference methods for the calibration of stochastic dynamo models', in 4th Solar Dynamo Thinkshop, Rome, Italy, 25 - 26 November 2019.
• Weyland, M. et al. (2018) 'Dynamic DNA damage and repair modeling : bridging the gap between experimental damage readout and model structure', in XIII International Workshop on Artificial Life and Evolutionary Computation (WIVACE), Parma, Italy, 10-12 September 2018.
• Ulzega, S. and Albert, C. (2018) 'Bayesian parameter inference with stochastic solar dynamo models', in NDES 2018, 26th Nonlinear Dynamics of Electronic Systems Conference, Acireale, Italy, June, 11-13 2018.

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