Laura Scarabosio

scarabosio@science.ru.nl

I am a tenure track assistant professor at the Radboud University working in numerical analysis and uncertainty quantification.

Research interests

  • Shape uncertainty
  • Random multiscale materials
  • Multilevel methods
  • Deep neural networks
  • Bayesian inversion
  • Applications to medicine and biology

    • Publications

    Preprints

  • S. Schönfeld, A. Ozkan, L. Scarabosio, M. N. Rylander, C. Kuttler Environmental stress level to model tumor cell growth and survival
  • L. Scarabosio, Deep neural network surrogates for non-smooth quantities of interest in shape uncertainty quantification

    • Articles in journals

  • 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

  • Radboud Summer School "Quantifying Uncertainty: Prediction and Inverse Problems" (upcoming, 8-12 August 2022)
  • Minisymposium "Shape Uncertainty Quantification meets Shape Statistics" at SIAM UQ22, with M. Zhang (upcoming, 12-15 April 2022)
  • Workshop "Nonlinear PDEs: Analysis & Simulation", with R. Cristoferi and V. Nikolić (upcoming, 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

  • TU/e colloquium, "Shape uncertainty quantification for interface problems" (November 2021).
  • Desda student colloquium, Radboud University, "Are you sure?" (November 2021).
  • DMV-ÖMG annual meeting, University of Passau (online), "A subdiffusive tumour growth model with fractional time derivative" (September 2021).
  • SIMAI 2020+21, University of Parma, "Deep neural network surrogates in shape uncertainty quantification" (September 2021).
  • NDNS+ workshop, University of Twente (online), "Deep neural network surrogates in shape uncertainty quantification" (June 2021).
  • One World Stochastic Numerics and Inverse Problems Seminar (online), "Shape uncertainty quantification for non-smooth quantities of interest" (June 2021).
  • CRUNCH seminar, Brown Univerisity (online), "Deep neural network surrogates for non-smooth quantities of interest in shape uncertainty quantification" (March 2021).

    • Curriculum vitae

    Professional experience

  • Aug 2020 - present: tenure track 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).

    • Teaching

    Courses

  • 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.

    • Supervision

  • Jan 2020 - Jul 2020: co-supervision of Master thesis of Alexandra Starostina, "Robin boundary conditions for PDE-based sampling of Gaussian Random Fields", TU Munich.