Dr. Simone Ulzega

Projects

• Feature Learning for Bayesian Inference / Team member / ongoing
• Machine learning for NMR spectroscopy / Team member / completed
• Data mining in neurological medicine / Project leader / completed
• Digital simulation for the tailored production of 3D nanofiber filters and their integration in a full protective suit for pandemic cases / Team member / completed
• BISTOM – Bayesian Inference with Stochastic Models / Project leader / completed
• A cloud-based IoT approach for food safety and quality prediction / Team member / completed
• Advanced Bayesian inference with stochastic hydrological models / Team member / completed

Publications

Articles in scientific journal, peer-reviewed

• Reda, R., Penza, V., Criscuoli, S., Bertello, L., Cantoresi, M., Lucaferri, L., Ulzega, S., & Berrilli, F. (2025). Modeling decadal and centennial solar UV irradiance changes. Solar Physics, 300(173). https://doi.org/10.1007/s11207-025-02572-3
• Ulzega, S., Beer, J., Ferriz-Mas, A., Dirmeier, S., & Albert, C. (2025). Shedding light on the solar dynamo using data-driven Bayesian parameter inference. The Astrophysical Journal, 992(1), 61. https://doi.org/10.3847/1538-4357/adfec3
• Penza, V., Bertello, L., Cantoresi, M., Criscuoli, S., Lucaferri, L., Reda, R., Ulzega, S., & Berrilli, F. (2024). Reconstruction of the total solar irradiance during the last millennium. The Astrophysical Journal, 976(1), 11. https://doi.org/10.3847/1538-4357/ad7c49
• Ulzega, S., & 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), 2935–2950. https://doi.org/10.5194/hess-27-2935-2023
• Bacci, M., Sukys, J., Reichert, P., Ulzega, S., & Albert, C. (2023). A comparison of numerical approaches for statistical inference with stochastic models. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-023-02434-z
• Albert, C., Ulzega, S., Ozdemir, F., Perez-Cruz, F., & Mira, A. (2022). Learning summary statistics for Bayesian inference with autoencoders. SciPost Physics Core, 5(3), 43. https://doi.org/10.21468/SciPostPhysCore.5.3.043
• Castelnovo, A., Amacker, J., Maiolo, M., Amato, N., Pereno, M., Riccardi, S., Danani, A., Ulzega, S., & Manconi, M. (2022). High-density EEG power topography and connectivity during confusional arousal. Cortex, 155, 62–74. https://doi.org/10.1016/j.cortex.2022.05.021
• Albert, C., Ferriz-Mas, A., Gaia, F., & Ulzega, S. (2021). Can stochastic resonance explain recurrence of Grand Minima? The Astrophysical Journal Letters, 916(2), L9. https://doi.org/10.3847/2041-8213/ac0fd6
• Maiolo, M., Ulzega, S., Gil, M., & Anisimova, M. (2020). Accelerating phylogeny-aware alignment with indel evolution using short time Fourier transform. NAR Genomics and Bioinformatics, 2(4), lqaa092. https://doi.org/10.1093/nargab/lqaa092
• Weyland, M. S., Thumser-Henner, P., Nytko, K. J., Rohrer Bley, C., Ulzega, S., Petri-Fink, A., Lattuada, M., Füchslin, R. M., & Scheidegger, S. (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. https://doi.org/10.1155/2020/5972594
• Cousin, S., Charlier, C., Kadeřávek, P., Marquardsen, T., Tyburn, J.-M., Bovier, P.-A., Ulzega, S., Speck, T., Wilhelm, D., Engelke, F., Maas, W., Sakellariou, D., Bodenhausen, G., Pelupessy, P., & Ferrage, F. (2016). High-resolution two-field nuclear magnetic resonance spectroscopy. Physical Chemistry Chemical Physics, 18(48), 33187–33194. https://doi.org/10.1039/C6CP05422F
• Albert, C., Ulzega, S., & Stoop, R. (2016). Boosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separation. Physical Review E, 93(4). https://doi.org/10.1103/PhysRevE.93.043313

Written conference contributions, peer-reviewed

• Weyland, M., Thumser-Henner, P., Rohrer Bley, C., Ulzega, S., Petri-Fink, A., Lattuada, M., Scheidegger, S., & Füchslin, R. M. (2019). Dynamic DNA damage and repair modelling : bridging the gap between experimental damage readout and model structure [Conference paper]. In S. Cagnoni, M. Mordonini, R. Pecori, A. Roli, & M. Villani (Eds.), Artificial Life and Evolutionary Computation (pp. 127–137). Springer. https://doi.org/10.1007/978-3-030-21733-4_10
• Ulzega, S., & Albert, C. (2019). Bayesian inference for solar dynamo models. 1st Swiss “Workshop on Machine Learning for Environmental and Geosciences” (MLEG2019), Dübendorf, 16-17 January 2019.

Other publications

• Ulzega, S., & Albert, C. (2019). Bayesian parameter inference with stochastic solar dynamo models. Platform for Advanced Scientific Computing (PASC19), Zurich, 12-14 June 2019. https://doi.org/10.21256/zhaw-3225
• Ulzega, S. (2018). Calibrating stochastic models for understanding solar activity. Transfer, 2018(1), 8. https://doi.org/10.21256/zhaw-3851
• Ulzega, S. (2017, August 31). Boosting Bayesian parameter inference of stochastic differential equation models. Life in Numbers 3, Wädenswil, 31 August 2017.
• Ulzega, S. (2017). Boosting parameter inference with stochastic models using molecular dynamics and high-performance computing.

Oral conference contributions and abstracts

• Ulzega, S., Albert, C., & Beer, J. (2024, November 29). New insights on the sun through the calibration of solar dynamo models on millennial records of magnetic activity. 6th Swiss SCOSTEP Workshop, Locarno, Switzerland, 28-29 November 2024.
• Ulzega, S., & Albert, C. (2024, September 17). Shedding light on the sun through the calibration of solar dynamo models on millennial records of solar activity. 28th Nonlinear Dynamics of Electronic Systems Conference (NDES), Grächen, Switzerland, 16-18 September 2024.
• Ulzega, S., Albert, C., & Beer, J. (2023, May 16). Shedding light on the sun through the calibration of solar dynamo models on millennial records of solar activity. 5th Swiss SCOSTEP Workshop, Windisch, Switzerland, 15-16 May 2023. http://scostep2023.cs.technik.fhnw.ch/pres/ulzega_scostep_2023.pdf
• Ulzega, S., & Albert, C. (2023, March 16). Boosting Bayesian parameter inference of SDE models by Hamiltonian scale separation : a real-world case study in urban hydrology. 3rd Biennial Meeting of the ISBA Section on Bayesian Computation (Bayes Comp), Levi, Finnland, 15-17 March 2023.
• Ulzega, S., & Albert, C. (2022). Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model. EGU General Assembly 2022, Vienna, Austria, 23-27 May 2022. https://doi.org/10.5194/egusphere-egu22-8729
• Albert, C., & Ulzega, S. (2020). Stochastic resonance could explain recurrence of Grand Minima. EGU General Assembly 2020, Online, 4-8 May 2020. https://doi.org/10.5194/egusphere-egu2020-15185
• Albert, C., Gaia, F., & Ulzega, S. (2020, May). Can stochastic resonance explain the amplification of planetary tidal forcing? EGU General Assembly 2020, Online, 4-8 May 2020. https://presentations.copernicus.org/EGU2020/EGU2020-15185_presentation.pdf
• Albert, C., & Ulzega, S. (2019). Can stochastic resonance explain the amplification of planetary tidal forcing? 4th Solar Dynamo Thinkshop, Rome, Italy, 25 - 26 November 2019.
• Ulzega, S., & Albert, C. (2019). Bayesian inference methods for the calibration of stochastic dynamo models. 4th Solar Dynamo Thinkshop, Rome, Italy, 25 - 26 November 2019.
• Weyland, M., Thumser-Henner, P., Rohrer Bley, C., Ulzega, S., Petri-Fink, A., Lattuada, M., Scheidegger, S., & Füchslin, R. M. (2018). Dynamic DNA damage and repair modeling : bridging the gap between experimental damage readout and model structure. XIII International Workshop on Artificial Life and Evolutionary Computation (WIVACE), Parma, Italy, 10-12 September 2018.
• Ulzega, S., & Albert, C. (2018, June 12). Bayesian parameter inference with stochastic solar dynamo models. NDES 2018, 26th Nonlinear Dynamics of Electronic Systems Conference, Acireale, Italy, June, 11-13 2018.
• Ulzega, S., Albert, C., Abreu, J., & Beer, J. (2018, May 7). Is there a planetary influence on solar activity? Some insights from nonlinear dynamics and Bayesian statistics. Biannual Workshop on Solar Physics, Freiburg, Germany, 7-9 May 2018.
• Ulzega, S. (2018, January 10). A Hamiltonian Monte Carlo method for boosting Bayesian parameter inference of stochastic differential equation models. Kick-off Workshop for SDSC-ETH BISTOM Project at Eawag, Dübendorf, Germany, 10 January 2018.
• Ulzega, S. (2017, June). A Hamiltonian Monte Carlo method for boosting Bayesian parameter inference of stochastic differential equation models. NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017.

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