Uppsala universitet 
Stefan Engblom
Short CV: (pdf)
Publications: (pdf)

Stefan Engblom



I am particularly interested in Scientific Computing in the intersection with Data-driven research and Data Science. I have extensive experiences in many aspects of Scientific Computing in general, in Numerical Modeling and -Analys in particular, as well as to some extent in High-Performance Computing. My main focus of applications are in the Biosciences at broad, but I've also taken an interest in traditional computational Engineering applications.

Current active research projects include Bayesian approaches for compute intensive data-driven models in epidemics, including in particular prediction, and multiscale modeling and parameterization of living cells, where spatial stochasticity is an important aspect of the modeling.

Stefan Engblom

I am currently the main supervisor for 3 PhD-students:
  • Erik Blom (started 2021). Eriks project is Scalable computational modeling of living cells.
  • Gesina Menz (started 2022). Gesinas project is Data-driven modeling of living cells.
  • Vaishnavi Divya Shridar (started 2024). Divyas project is Towards model-based analysis in wastewater epidemiology: the specifics of antimicrobial resistance.
I was the main advisor of I am also the secondary advisor for Anna Frigge, Alfred Andersson, and Helena Andersson. I was previously the secondary advisor of MSc/BSc-theses: Find me at the Mathematics Genealogy Project...
...at the Research Gate...
...at Google Scholar...
...at ORCID....
...at ResearcherID....
...at GitHub.


I became an Associate Professor in 2014, being previously promoted to Docent in 2013 in Scientific computing. I originally joined UPMARC in 2011 with the aim at bringing problems from Scientific Computing into a form suitable to modern multicore/manycore computers, and vice versa, to develop and analyze algorithms and techniques suitable to such cards with interesting applications in mind. Research outputs here include, amongst others,machine learning methods in imaging with X-ray lasers, auto-tuning in CPU/GPU implementations of adaptive fast multipole methods, and shared memory approaches for event-based algorithms.

The Linnaeus center of excellence UPMARC
⇒ Focus area Application Performance
⇒ ⇒ Project group Parallel Algorithms

Before that, I was a PostDoc at the the Linné FLOW Centre where I started in September 2009 to work on computational modeling of multiphase flow for two immiscible fluids and a surface active agent. For example, this would be the correct model when considering a mixture of oil/water and a detergent.

Before that I was also briefly involved in Anna-Karin Tornberg's project concerning simulating fibers suspended in fluids.

As a graduate student I studied methods for computing numerical solutions to stochastic descriptions of chemical reactions. The underlying mathematical description is a continuous-time Markov chain and the equation governing the probability density is called the Master Equation. Unfortunately, the master equation cannot be solved numerically for more than, say, five molecular species due to the exponential growth of work and memory requirements ('curse of dimensionality'). Stochastic descriptions of chemical reactions are needed to describe the chemical processes taking place inside living cells with few copy numbers of each molecular species. Usual models for cell simulation are based on the reaction rate equations which form a system of nonlinear ordinary differential equations. Such models ignore the stochastic fluctuations in the cells and are therefore less accurate.

Computational systems biology group.

E. Coli meshE. Coli conc

I was also involved in a project joint with the Division for Electricity and Lightning Research, Uppsala University called "Electric power generation from winds". The ultimate goal of the project is to provide more efficient wind turbines. I have been working together with Paul Deglaire and, lately, Anders Goude. The work has resulted in a two-dimensional random vortex method simulating fluid flows around general airfoils at a quite high Reynolds number. My contribution has been focused around a user-friendly and very efficient implementation of the fast multipole method.


See also: DiVA records for Author: Stefan Engblom.
Publication list (pdf)
  1. E. Blom, S. Engblom: Morphological stability for in silico models of avascular tumors

    • In Bull. Math. Biol, 86 (2024) (doi).
    • Available via arXiv.
  2. E. Blom, S. Engblom, G. Menz: Modeling the hallmarks of avascular tumors

  3. 2023

  4. R. Marin, H. Runvik, A. Medvedev, and S. Engblom: Bayesian Monitoring of COVID-19 in Sweden

  5. S. Bronstein, S. Engblom, and R. Marin: Bayesian inference in Epidemics: linear noise analysis

  6. 2022

  7. B. Kennedy, H. Fitipaldi, U. Hammar, et al.: App-based COVID- 19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden

  8. F. Wrede, R. Eriksson, R. Jiang, L. Petzold, S. Engblom, A. Hellander, and P. Singh: Robust and integrative Bayesian neural networks for likelihood-free parameter inference


  9. J. Bull and S. Engblom: Distributed and Adaptive Fast Multipole Method In Three Dimensions

  10. S. Engblom, R. Eriksson, P. Vilanova: Towards Confident Bayesian Parameter Estimation in Stochastic Chemical Kinetics

    • In Numerical Mathematics and Advanced Applications: ENUMATH 2019, Lecture Notes in Computational Science and Engineering 139, 373--380 (2021): (doi).
    • DiVA record.
  11. 2020

  12. J. Liu, S. Engblom, C. Nettelblad: Flash X-ray diffraction imaging in 3D: a proposed analysis pipeline

  13. S. Engblom, R. Eriksson, and S. Widgren: Bayesian epidemiological modeling over high-resolution network data

  14. H. Runvik, A. Medvedev, R. Eriksson, and S. Engblom: Initialization of a disease transmission model

    • In Proceedings of the 3rd IFAC Workshop on Cyber-Physical & Human Systems, IFAC-PapersOnLine 53(5):839--844 (2020) (doi).
    • Available via arXiv.
    • DiVA record.
  15. S. Engblom et al.: The URDME Manual version 1.4

  16. 2019

  17. S. Widgren, P. Bauer, R. Eriksson, and S. Engblom: SimInf: An R package for data-driven stochastic disease spread simulations

  18. J. Lindén, P. Bauer, S. Engblom, and B. Jonsson: Exposing inter-process information for efficient PDES of spatial stochastic systems on multicores

    • In ACM Trans. Model. Comput. Simul., 29(2):11:1--25 (2019) (doi).
    • DiVA record.
  19. J. Liu, G. Schot, S. Engblom: Supervised Classification Methods for Flash X-ray single particle diffraction Imaging

  20. S. Engblom: Stochastic simulation of pattern formation in growing tissue: a multilevel approach

  21. D. Arjmand, S. Engblom, G. Kreiss: Temporal upscaling in micro magnetism via heterogeneous multiscale methods

  22. S. Widgren, T. Rosendal, S. Engblom, and K. Ståhl: SimInf for spatio-temporal data-driven modeling of African swine fever in Swedish wildboar

    • In GeoVet 2019: Novel spatio-temporal approaches in the era of Big Data (url)


  23. S. Engblom, P. Lötstedt, L. Meinecke: Mesoscopic Modeling of Random Walk and Reactions in Crowded Media

  24. S. Widgren, S. Engblom, U. Emanuelson, and A. Lindberg: Spatio-temporal modelling of verotoxigenic Escherichia coli O157 in cattle in Sweden: Exploring options for control

  25. S. Engblom, D. Wilson, R. Baker: Scalable population-level modeling of biological cells incorporating mechanics and kinetics in continuous time

  26. P. Bauer, S. Engblom, S. Mikulovic, and A. Senek: Multiscale modeling via split-step methods in neural firing

  27. J. Liu, S. Engblom, and C. Nettelblad: Assessing Uncertainties in X-ray Single-particle Three-dimensional reconstructions

  28. A. Goude, S. Engblom: A general high order two-dimensional panel method

  29. A. Chevallier and S. Engblom: Pathwise error bounds in multiscale variable splitting methods for spatial stochastic kinetics

  30. J. Lindén, P. Bauer, S. Engblom, B. Jonsson: Fine-Grained Local Dynamic Load Balancing in PDES

    • In Proceedings of the 2018 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, SIGSIM PADS '18, pages 201--2012 (2018): (doi).
    • DiVA record.
  31. R. Eriksson, S. Engblom, and S. Widgren: Towards Bayesian parametrization of national scale epidemics

    • In MATHMOD 2018 Extended Abstract Volume, ARGESIM(55), p. 65--66 (2018): (doi)
    • DiVA record.
  32. 2017

  33. S. Engblom and S. Widgren: Data-driven computational disease spread modeling: from measurement to parametrisation and control

  34. S. Engblom: Stability and Strong Convergence for Spatial Stochastic Kinetics

  35. S. Engblom, A. Hellander, P. Lötstedt: Multiscale Simulation of Stochastic Reaction-Diffusion Networks

  36. G. Christoffersson, J. Lomei, P. O'Callaghan, J. Kreuger, S. Engblom, and M. Phillipson: Vascular sprouts induce local attraction of proangiogenic neutrophils

  37. J. Lindén, P. Bauer, S. Engblom, B. Jonsson: Exposing inter-process information for efficient parallel discrete event simulation of spatial stochastic system

    • In Proceedings of the 2017 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, SIGSIM PADS '17, pages 53--64, (2017): (doi).
    • DiVA record.
  38. A. Senek and S. Engblom: Multiscale Stochastic Neuron Modeling - with Applications in Deep Brain Stimulation (Wip)

    • In Proceedings of the Summer Simulation Multi-Conference, Summer-Sim '17, pages p. 38:1--38:5 (2017): (ACM URL)
    • DiVA record.
  39. P. Bauer, S. Engblom: The URDME Manual version 1.3

  40. 2016

  41. S. Widgren, S. Engblom, P. Bauer, J. Frössling, U. Emanuelson, and A. Lindberg: Data-driven network modelling of disease transmission using complete population movement data: spread of VTEC O157 in Swedish cattle

  42. S. Engblom, D. Lukarski: Fast Matlab compatible sparse assembly on multicore computers

  43. E. Blanc, S. Engblom, A. Hellander, P. Lötstedt: Mesoscopic modeling of stochastic reaction-diffusion kinetics in the subdiffusive regime

  44. P. Bauer, S. Engblom, S. Widgren: Fast event-based epidemiological simulations on national scales

  45. L. Meinecke, S. Engblom, A. Hellander, and P. Lötstedt: Analysis and design of jump coefficients in discrete stochastic diffusion models

  46. S. Engblom and V. Sunkara: Preconditioned Metropolis sampling as a strategy to improve efficiency in posterior exploration

  47. J. Bull, S. Engblom, and S. Holmgren: A direct solver for the advection-diffusion equation using Green's functions and low-rank approximation

    • In proceedings of the 7th ECCOMAS Congress, European Community on Computional Methods in Applied Sciences (ECCOMAS 2016): (direct link).
    • DiVA record.
  48. 2015

  49. A. Milias-Argeitis, S. Engblom, P. Bauer, M. Khammash: Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks

  50. S. Engblom: Strong convergence for split-step methods in stochastic jump kinetics

  51. T. Ekeberg, S. Engblom, J. Liu: Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters

  52. P. Bauer, J. Lindén, S. Engblom, B. Jonsson: Efficient interprocess synchronization for parallel discrete event simulation on multicores

    • In Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, SIGSIM PADS '15, pages 183--194, (2015): (doi).
    • DiVA record.
  53. P. Bauer, S. Engblom: Sensitivity estimation and inverse problems in spatial stochastic models of chemical kinetics

    • In Numerical Mathematics and Advanced Applications: ENUMATH 2013, Lecture Notes in Computational Science and Engineering 103, 519--527 (2015): (doi).
    • DiVA record.
  54. 2014

  55. S. Engblom: On the stability of stochastic jump kinetics

  56. M. Holm, S. Engblom, A. Goude, S. Holmgren: Dynamic autotuning of adaptive fast multipole methods on hybrid multicore CPU & GPU systems

  57. S. Engblom, J. Liu: X-ray laser imaging of biomolecules using multiple GPUs

    • In Parallel Processing and Applied Mathematics, Lecture Notes in Computer Science:480--489, 2014: (doi).
    • DiVA record.
  58. S. Engblom, J. Pender: Approximations for the moments of nonstationary and state dependent birth-death queues

  59. 2013

  60. A. Goude and S. Engblom: Adaptive fast multipole methods on the GPU

  61. S. Engblom, M. Do-Quang, G. Amberg, A-K. Tornberg: On diffuse interface modeling and simulation of surfactants in two-phase fluid flow

  62. K. Mattsson, M. Almquist, S. Engblom: Stable and accurate wave simulations in complex geometries and discontinuous media

    • In proceedings of the 11th International Conference on Mathematical and Numerical Aspects of Waves (WAVES 2013), (direct link).
    • DiVA record.
  63. M. Do-Quang, S. Engblom, A-K. Tornberg, G. Amberg: The well-posedness of diffuse interface modeling of surfactants in two-phase fluid flow

    • In proceedings of the 1st International workshop on Wetting and evaporation: droplets of pure and complex fluids
    • DiVA record.
  64. 2012

  65. B. Drawert, S. Engblom, and A. Hellander: URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries

  66. P. Bauer, B. Drawert, S. Engblom, A. Hellander: URDME v. 1.2: User's manual

  67. P. Bauer, S. Mikulovic, S. Engblom, K. E. Leao, F. Rattay, and R. N. Leao: Finite element analysis of neuronal electric fields: the effect of heterogeneous resistivity

  68. 2011

  69. S. Engblom: On well-separated sets and fast multipole methods

  70. B. Drawert, S. Engblom, A. Hellander: URDME v. 1.1: User's manual

  71. Before 2010

  72. (2009) S. Engblom: Parallel in Time Simulation of Multiscale Stochastic Chemical Kinetics

    • In Multiscale Model. Simul. 8(1):46--68, 2009: (doi)
      Reached #2 and #3 at the top 20 list of most downloaded papers in Multiscale Model. Simul. for the months October and September 2009, respectively.
    • DiVA record.
    • Review: MR2575044
    • Technical report: (abstract), (pdf)
    • Preprint at arXiv.
  73. (2009) S. Engblom, L. Ferm, A. Hellander, P. Lötstedt: Simulation of Stochastic Reaction-Diffusion Processes on Unstructured Meshes

  74. (2009) S. Engblom: Spectral Approximation of Solutions to the Chemical Master Equation

    • In J. Comput. Appl. Math. 229(1):208--221, 2009: (doi)
    • DiVA record.
    • Review: MR2522514 (Unfortunately, this review is not accurate)
  75. (2009) S. Engblom: Galerkin Spectral Method applied to the Chemical Master Equation

  76. (2009) P. Deglaire, S. Engblom, O. Ågren, H. Bernhoff: Analytical solutions for a single blade in vertical axis turbine motion in two-dimensions

    • In Eur. J. Mech. B Fluids 28(4):506--520, 2009: (doi)
      The #1 most downloaded paper in Eur. J. Mech. B Fluids during the period April 2009--March 2011!
    • DiVA record.
  77. (2008) S. Engblom, PhD-thesis: Numerical Solution Methods in Stochastic Chemical Kinetics

  78. (2008) S. Engblom: Time-parallel simulation of stochastic chemical kinetics

    • In proceedings of the International Conference on Numerical Analysis and Applied Mathematics (ICNAAM 2008) (doi)
    • DiVA record.
  79. (2008) S. Engblom, J. Cullhed, A. Hellander: The URDME Manual version 1.0

  80. (2006) S. Engblom, Licentiate-thesis: Numerical methods for the chemical master equation

  81. (2006) S. Engblom: Computing the Moments of High Dimensional Solutions of the Master Equation

  82. (2006) S. Engblom: Gaussian quadratures with respect to discrete measures

AMS Mathematical reviews

See also: AMS-MR by
Reviewer: Stefan Engblom
Author: Stefan Engblom
  • MR3022034: "Error bound for piecewise deterministic processes modeling stochastic reaction systems" by T. Jahnke and M. Kreim. Personal copy.

  • MR2972594: "Asymptotic stability of balanced methods for stochastic jump-diffusion differential equations" by L. Hu, S. Gan, and X. Wang. Personal copy.

  • MR2902602: "Multilevel Monte Carlo for continuous time Markov chains, with applications in biochemical kinetics" by D. F. Anderson and D. J. Higham. Personal copy.

  • MR2895415: "Error analysis of tau-leap simulation methods" by D. F. Anderson, A. Ganguly, and T. G. Kurtz. Personal copy.

  • MR2846497: "Towards automatic global error control: computable weak error expansion for the tau-leap method" by J. Karlsson and R. Tempone. Personal copy.

  • MR2828008: "On Markov state models for metastable processes" by N. Djurdjevac, M. Sarich, and C. Schütte. Personal copy.

  • MR2774238: "Convergence of numerical approximation for jump models involving delay and mean-reverting square root process" by F. Jiang, Y. Shen, and F. Wu. Personal copy.

  • MR2603888: "Chebyshev methods with discrete noise: the tau-ROCK methods" by A. Abdulle, Y. Hu and T. Li. Personal copy.

  • MR2598780: "Forty-five years of A-stability" by J. C. Butcher. Personal copy.

  • MR2505843: "Krylov subspace spectral methods for the time-dependent Schrödinger equation with non-smooth potentials" by J. V. Lambers. Personal copy.

Stefan Engblom


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