Dr. Simone Ulzega

Projects

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

Publications

• Bacci, Marco; Sukys, Jonas; Reichert, Peter; Ulzega, Simone; Albert, Carlo,

  2023.

  A comparison of numerical approaches for statistical inference with stochastic models.

  Stochastic Environmental Research and Risk Assessment.

  Available from: https://doi.org/10.1007/s00477-023-02434-z

• Ulzega, Simone; Albert, Carlo,

  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.

  Available from: https://doi.org/10.5194/hess-27-2935-2023

• Castelnovo, Anna; Amacker, Julian; Maiolo, Massimo; Amato, Ninfa; Pereno, Matteo; Riccardi, Silvia; Danani, Andrea; Ulzega, Simone; Manconi, Mauro,

  2022.

  High-density EEG power topography and connectivity during confusional arousal.

  Cortex.

  (155), pp. 62-74.

  Available from: https://doi.org/10.1016/j.cortex.2022.05.021

• Albert, Carlo; Ulzega, Simone; Ozdemir, Firat; Perez-Cruz, Fernando; Mira, Antonietta,

  2022.

  Learning summary statistics for Bayesian inference with autoencoders.

  SciPost Physics Core.

  5(3), pp. 043.

  Available from: https://doi.org/10.21468/SciPostPhysCore.5.3.043

• Albert, Carlo; Ferriz-Mas, Antonio; Gaia, Filippo; Ulzega, Simone,

  2021.

  Can stochastic resonance explain recurrence of Grand Minima?.

  The Astrophysical Journal Letters.

  916(2), pp. L9.

  Available from: https://doi.org/10.3847/2041-8213/ac0fd6

• Maiolo, Massimo; Ulzega, Simone; Gil, Manuel; Anisimova, Maria,

  2020.

  Accelerating phylogeny-aware alignment with indel evolution using short time Fourier transform.

  NAR Genomics and Bioinformatics.

  2(4), pp. lqaa092.

  Available from: https://doi.org/10.1093/nargab/lqaa092

• Weyland, Mathias S.; Thumser-Henner, Pauline; Nytko, Katarzyna J.; Rohrer Bley, Carla; Ulzega, Simone; Petri-Fink, Alke; Lattuada, Marco; Füchslin, Rudolf Marcel; Scheidegger, Stephan,

  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.

  Available from: https://doi.org/10.1155/2020/5972594

• Albert, Carlo; Ulzega, Simone; Stoop, Ruedi,

  2016.

  Boosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separation.

  Physical Review E.

  93(4).

  Available from: https://doi.org/10.1103/PhysRevE.93.043313

• Cousin, Samuel; Charlier, Cyril; Kadeřávek, Pavel; Marquardsen, Thorsten; Tyburn, Jean-Max; Bovier, Pierre-Alain; Ulzega, Simone; Speck, Thomas; Wilhelm, Dirk; Engelke, Frank; Maas, Werner; Sakellariou, Dimitrios; Bodenhausen, Geoffrey; Pelupessy, Philippe; Ferrage, Fabien,

  2016.

  High-resolution two-field nuclear magnetic resonance spectroscopy.

  Physical Chemistry Chemical Physics.

  18(48), pp. 33187-33194.

  Available from: https://doi.org/10.1039/C6CP05422F

• Ulzega, Simone; Albert, Carlo,

  2019.

  Bayesian inference for solar dynamo models [poster].

  In:

  1st Swiss “Workshop on Machine Learning for Environmental and Geosciences” (MLEG2019), Dübendorf, 16-17 January 2019.

• Weyland, Mathias; Thumser-Henner, Pauline; Rohrer Bley, Carla; Ulzega, Simone; Petri-Fink, Alke; Lattuada, Marco; Scheidegger, Stephan; Füchslin, Rudolf Marcel,

  2019.

  Dynamic DNA damage and repair modelling : bridging the gap between experimental damage readout and model structure [paper].

  In:

  Cagnoni, Stefano; Mordonini, Monica; Pecori, Riccardo; Roli, Andrea; Villani, Marco, eds.,

  Artificial Life and Evolutionary Computation.

  XIII International Workshop on Artificial Life and Evolutionary Computation (WIVACE), Parma, Italy, 10-12 September 2018.

  Cham:

  Springer.

  pp. 127-137.

  Communications in Computer and Information Science ; 900.

  Available from: https://doi.org/10.1007/978-3-030-21733-4_10

• Ulzega, Simone; Albert, Carlo,

  2019.

  Bayesian parameter inference with stochastic solar dynamo models [poster].

  In:

  Platform for Advanced Scientific Computing (PASC19), Zurich, 12-14 June 2019.

  ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

  Available from: https://doi.org/10.21256/zhaw-3225

• Ulzega, Simone,

  2018.

  Calibrating stochastic models for understanding solar activity.

  Transfer.

  2018(1), pp. 8.

  Available from: https://doi.org/10.21256/zhaw-3851

• Ulzega, Simone,

  2017.

  Boosting Bayesian parameter inference of stochastic differential equation models [poster].

  In:

  Life in Numbers 3, Wädenswil, 31 August 2017.

• Ulzega, Simone,

  2017.

  Boosting parameter inference with stochastic models using molecular dynamics and high-performance computing.

• Ulzega, Simone; Albert, Carlo; Beer, Jürg,

  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 from: http://scostep2023.cs.technik.fhnw.ch/pres/ulzega_scostep_2023.pdf

• Ulzega, Simone; Albert, Carlo,

  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.

• Ulzega, Simone; Albert, Carlo,

  2022.

  Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model.

  In:

  EGU General Assembly 2022, Vienna, Austria, 23-27 May 2022.

  Available from: https://doi.org/10.5194/egusphere-egu22-8729

• Albert, Carlo; Gaia, Filippo; Ulzega, Simone,

  2020.

  Can stochastic resonance explain the amplification of planetary tidal forcing?.

  In:

  EGU General Assembly 2020, Online, 4-8 May 2020.

  Available from: https://presentations.copernicus.org/EGU2020/EGU2020-15185_presentation.pdf

• Albert, Carlo; Ulzega, Simone,

  2020.

  Stochastic resonance could explain recurrence of Grand Minima.

  In:

  EGU General Assembly 2020, online, 4-8 May 2020.

  Available from: https://doi.org/10.5194/egusphere-egu2020-15185

• Ulzega, Simone; Albert, Carlo,

  2019.

  Bayesian inference methods for the calibration of stochastic dynamo models.

  In:

  4th Solar Dynamo Thinkshop, Rome, Italy, 25 - 26 November 2019.

• Albert, Carlo; Ulzega, Simone,

  2019.

  Can stochastic resonance explain the amplification of planetary tidal forcing?.

  In:

  4th Solar Dynamo Thinkshop, Rome, Italy, 25 - 26 November 2019.

• Ulzega, Simone; Albert, Carlo,

  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.

• Ulzega, Simone; Albert, Carlo; Abreu, Josè; Beer, Jürg,

  2018.

  Is there a planetary influence on solar activity? Some insights from nonlinear dynamics and Bayesian statistics.

  In:

  Biannual Workshop on Solar Physics, Freiburg, Germany, 7-9 May 2018.

• Ulzega, Simone,

  2018.

  A Hamiltonian Monte Carlo method for boosting Bayesian parameter inference of stochastic differential equation models.

  In:

  Kick-off Workshop for SDSC-ETH BISTOM project at Eawag, Dübendorf, Germany, 10 January 2018.

• Weyland, Mathias; Thumser-Henner, Pauline; Rohrer Bley, Carla; Ulzega, Simone; Petri-Fink, Alke; Lattuada, Marco; Scheidegger, Stephan; Füchslin, Rudolf Marcel,

  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, Simone,

  2017.

  A Hamiltonian Monte Carlo method for boosting Bayesian parameter inference of stochastic differential equation models.

  In:

  NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017.

go back