Education Profile

  • 2007 – 2012: PhD student in Numerical Analysis at the Royal Institute of Technology, KTH, Stockholm, Sweden.
  • 2006: Master in Computational Science at the University of Oslo, Norway.
  • 2004: Bachelor in Computational Science at the University of Oslo, Norway.

Research Interests

  • Stochastic differential equations
  • Multilevel Monte Carlo methods
  • Nonlinear filtering 
  • Molecular dynamics and the Schrödinger equation

Professional Profile

  • 2018 - ... Visiting researcher KAUST
  • 2017 - 2018 Guest teacher University of Gothenburg and Chalmers 
  • 2016 - 2017 Postdoc EPFL
  • 2014 - 2016 Postdoc University of Oslo 
  • ​2012 - 2014 Postdoc researcher KAUST

Håkon Hoel is a visiting researcher in the Stochastic Numerics Group at KAUST. He holds an MSc degree in Computational Science from the University of Oslo (2016) and a PhD in Numerical Analysis from KTH Royal Institute of Technology (2012). His main research interests are numerical analysis of stochastic differential equations, nonlinear filtering and multilevel Monte Carlo (MLMC) methods. His research interests also include different aspects of implementation and error estimation of stochastic models in the following areas: adaptive weak approximation of stochastic differential equations, numerical methods for the Schrödinger equation,  stochastic modeling and numerical algorithms for wireless channels.

Selected Publications

  • H. Hoel, K.J. Law, and R. Tempone, Multilevel ensemble Kalman filtering. SIAM Journal on Numerical Analysis, 54(3), 1813-1839 (2016).
  • H. Hoel, E. von Schwerin, A. Szepessy, and R. Tempone. Implementation and Analysis of an Adaptive Multilevel Monte Carlo Algorithm. Monte Carlo Methods Appl. 2014; 20 (1):1-41.
  • C. Bayer, H. Hoel, E. von Schwerin, and R. Tempone. On nonasymptotic optimal stopping criteria in Monte Carlo simulations. SIAM Journal on Scientific Computing 2014, 36:2, A869-885.
  • C. Bayer,  H. Hoel, H., A. Kadir, P. Plecháč, P., M. Sandberg, and A. Szepessy. Computational error estimates for Born–Oppenheimer molecular dynamics with nearly crossing potential surfaces. Applied Mathematics Research eXpress, 2015(2), 329-417.
  • H. Hoel. A numerical scheme using multi-shockpeakons to compute solutions of the Degasperis--Procesi equation. Electron. J. Diff. Eqns., Vol. 2007, No. 100, pp. 1-22.