Laura Scarabosio

scarabosio@science.ru.nl

I am an assistant professor at Radboud University working in numerical analysis and uncertainty quantification.

Research interests

  • Shape uncertainty
  • Multilevel methods
  • Deep neural networks
  • Bayesian inverse problems
  • Time-harmonic wave propagation
  • Random multiscale materials
  • Applications to medicine, biology, and power cable operation.

    • Publications

    Preprints

  • S. Schönfeld, L. Scarabosio, A. Ozkan, M.N. Rylander, C. Kuttler, Using systemic modeling and Bayesian calibration to investigate the role of the tumor microenvironment on chemoresistance.
  • W. van Harten, L. Scarabosio, Exploiting locality in sparse polynomial approximation of parametric elliptic PDEs and application to parameterized domains.

    • Articles in journals

  • L. Scarabosio, Deep neural network surrogates for non-smooth quantities of interest in shape uncertainty quantification, SIAM/ASA Journal on Uncertainty Quantification, 10(3), 975-1011. Preprint
  • S. Schönfeld, A. Ozkan, L. Scarabosio, M. N. Rylander, C. Kuttler Environmental stress level to model tumor cell growth and survival, Mathematical Biosciences and Engineering, 2022, 19(6): 5509-5545.
  • M. Fritz, C. Kuttler, M.L. Rajendran, L. Scarabosio, B. Wohlmuth, On a subdiffusive tumour growth model with fractional time derivative, IMA Journal of Applied Mathematics 86.4 (2021): 688-729. Preprint
  • L. Scarabosio, B. Wohlmuth, J.T. Oden, D. Faghihi, Goal-oriented adaptive modeling of random heterogeneous media and model-based multilevel Monte Carlo methods, Computers & Mathematics with Applications 78.8 (2019): 2700-2718. Preprint
  • U. Khristenko, L. Scarabosio, P. Swierczynski, E. Ullmann, B. Wohlmuth, Analysis of Boundary Effects on PDE-Based Sampling of Whittle--Matérn Random Fields, SIAM/ASA Journal on Uncertainty Quantification 7 (3), 948-974. Preprint
  • L. Scarabosio, Multilevel Monte Carlo on a high-dimensional parameter space for transmission problems with geometric uncertainties, International Journal for Uncertainty Quantification 9.6 (2019). Preprint
  • R. Hiptmair, L. Scarabosio, C. Schillings, C. Schwab, Large deformation shape uncertainty quantification in acoustic scattering, Advances in Computational Mathematics, 1-44. Preprint
  • E.A.B.F. Lima, J.T. Oden, B. Wohlmuth, A. Shahmoradi, D.A. Hormuth II, T.E. Yankeelov, L. Scarabosio, T. Horger, Selection and validation of predictive models of radiation effects on tumor growth based on noninvasive imaging data, Computer methods in applied mechanics and engineering 327, 277-305. Preprint
  • A. Paganini, L. Scarabosio, R. Hiptmair, I. Tsukerman, Trefftz approximations: a new framework for nonreflecting boundary conditions, IEEE Transactions on Magnetics 52 (3), 1-4.

    • Theses

  • L. Scarabosio, Shape uncertainty quantification for scattering transmission problems, Diss. ETH Zurich, 2016.

    • Events

    Organization

  • CWI Semester Programme "Uncertainty Quantification for High-Dimensional Problems" in Fall 2024, with W. Edeling, O. Mula, P. Coveney, R. Dwight.
  • Member of organizing committee for Woudschoten 2024 (25-27 September).
  • Minisymposium "Learning High-Dimensional Functions: Approximation, Sampling, and Algorithms" at SIAM UQ24, with S. Brugiapaglia, N. Dexter, W. van Harten.
  • Minisymposium "Exploring Complexity in Life Sciences with Modeling and Simulations" at SIAM CSE23, with D. Avitabile, F. Cavallini, G. Lord.
  • Radboud Summer School "Quantifying Uncertainty: Prediction and Inverse Problems" (8-12 August 2022).
  • Minisymposium "Shape Uncertainty Quantification meets Shape Statistics" at SIAM UQ22, with M. Zhang.
  • Workshop "Nonlinear PDEs: Analysis & Simulation", with R. Cristoferi and V. Nikolić (24-25 March 2022)
  • Applied Analysis Seminar, Radboud University, with V. Nikolić.
  • Minisymposium "Efficient simulation of random fields and applications" at ENUMATH2019, with K. Podgórski.

    • Recent talks

  • Workshop at Mittag-Leffler Institute, "Exploiting locality in surrogates for solutions to PDEs on parameterized domains" (June 2024).
  • Joint VU/Leiden/Delft seminar, "A seamless integration of models and data to predict and understand tumor cell dynamics" (April 2024).
  • 94th GAMM annual meeting, Magdeburg, "Surrogate models for shape uncertainty quantification" (March 2024).
  • SIAM UQ 2024, Trieste, "Exploiting locality in sparse polynomial approximation of parametric PDEs" (February 2024).
  • Seminar in PDE and Applications, TU Delft, "Shape uncertainties: a case study for uncertainty quantification" (November 2023).
  • AIP 2023, Göttingen, "Advantages of locality in random field representations for shape UQ" (September 2023).
  • German SIAM Chapters Meet Algorithmic Optimization, "Forward and inverse shape uncertainty quantification" (plenary), Trier (July 2023).
  • 29th Biennial Numerical Analysis Conference, University of Strathclyde, "Forward UQ with locally-supported basis functions (June 2023).

    • Supervision

    Postdocs

  • Jan 2023 - Aug 2023: (with V. Nikolić and P. Korevaar) Julio Careaga.

    • PhD students

  • since Jan 2023: (with G. Lord and S. Rieken) Jordi de Lange, sponsored by Alliander.
  • since Nov 2021: Wouter van Harten.
  • May 2019 - Dec 2023: (with C. Kuttler) Sabrina Schönfeld, "Environmental stress level - a mathematical modeling framework to investigate the influence of the microenvironment on tumor cell survival". Link to PhD thesis.

    • Master theses

  • Jan 2023 - Jul 2023: (with W. van Harten) Safiere Kuijpers, "Bayesian Shape Inversion for Scattering Transmission Problems".
  • Jan 2023 - Jul 2023: Dirk Heldens, "Reducing uncertainty in cable temperature estimation", internship with Alliander (team of S. Rieken).
  • Jan 2020 - Jul 2020: (with B. Wohlmuth) Alexandra Starostina, "Robin boundary conditions for PDE-based sampling of Gaussian Random Fields".

    • Bachelor theses

  • Feb 2024 - Jul 2024: Janna van Assen, "Deep learning as optimal control problem".
  • Jan 2024 - Jun 2024: (with G. Lord) Houssam Larhdaf, "Stock price forcasting: a Kalman filter approach".
  • Jan 2022 - Dec 2022: (with G. Lord) Laura van Leuven, "Gradient descent methods and their use in machine learning".

    • Teaching

  • Spring 2024: Monte Carlo Methods, Radboud University.
  • Fall 2023: Numerieke Methoden, Radboud University.
           Bachelor seminar (with S. Tijssen), Radboud University.
  • Spring 2023: Monte Carlo Methods, Radboud University.
  • Fall 2022: Bachelor seminar (with S. Tijssen), Radboud University.
  • Spring 2022: Monte Carlo Methods, Radboud University.
  • Fall 2021: Bachelor seminar (with P. Hochs), Radboud University.
  • Spring 2021: Monte Carlo Methods, Radboud University.
  • Fall 2020: Bachelor seminar (with P. Hochs), Radboud University.
  • Fall 2019: Einführung in die Programmierung, TU Munich.
  • Fall 2017: Einführung in die Programmierung, TU Munich.
  • 2016 - 2020: teaching assistant for various courses in numerical analyis at TU Munich.
  • 2012 - 2016: teaching assistant for various courses in numerical analysis at ETH Zürich.

    • Curriculum vitae

    Professional experience

  • since Aug 2020: assistant professor, IMAPP, Radboud University (Netherlands).
  • Sep 2016 - Jul 2020: postdoc, Chair of Numerical Analysis, TU Munich (Germany).
  • May 2016 - Aug 2016: scientific assistant, Seminar for Applied Mathematics, ETH Zürich (Switzerland).

    • Education

  • Feb 2012 - May 2016: PhD in Mathematics, Seminar for Applied Mathematics, ETH Zürich (Switzerland).
  • Oct 2009 - Dic 2011: Master in Mathematical Modelling in Engineering, Polytechnic of Turin (Italy).
  • Sep 2006 - Oct 2009: Bachelor in Mathematics in Engineering, Polytechnic of Turin (Italy).