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

        

Research

I am interested in most aspects of Scientific Computing, in Numerical Modeling and -Analys in particular, and in High-Performance Computing. My main focus of applications are in the Biosciences at broad, but I've also taken an interest in Engineering applications, and in Data-driven research. Current active research projects include multiscale modeling and parameterization of living cells in a population, machine learning methods in imaging with X-ray lasers, auto-tuning in CPU/GPU implementations of adaptive fast multipole methods, and Bayesian approaches for compute intensive spatial stochastic data-driven models in epidemics.

Stefan Engblom

I am the main supervisor for 2 PhD-students:
  • Robin Eriksson (started 2017). Robins project is Computational Modeling, Parameterization, and Evaluation of the spread of Diseases and is joint with Stefan Widgren using the SimInf-software.
  • Jing Liu (started 2012). Jings project is entitled Parallel algorithms in imaging of biomolecules with X-ray lasers and is joint with Janos Hajdu's group at the Department of Cell and Molecular Biology.
I was the main advisor of I am also the secondary advisor for Adrien Coulier (2015-) and Markus de Ruijter. 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
arXiv...
...at GitHub.

Previously

I am currently an Associate Professor (since 2014) and Docent (since 2013) in Scientific computing. I previously 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.

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.

Publications

See also: DiVA records for Author: Stefan Engblom.
Publication list (pdf)

    2019

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

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

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

    • Accepted in GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data (url)
  4. S. Widgren, P. Bauer, R. Eriksson, and S. Engblom: SimInf: An R package for data-driven stochastic disease spread simulations

    • Accepted for publication in J. Stat. Softw. (2019).
    • Available via arXiv.
  5. 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.
  6. J. Liu, G. Schot, S. Engblom: Supervised Classification Methods for Flash X-ray single particle diffraction Imaging

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

  8. 2018

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

  10. 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

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

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

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

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

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

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

  17. 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.
  18. 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.
  19. 2017

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

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

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

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

  24. 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.
  25. 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.
  26. P. Bauer, S. Engblom: The URDME Manual version 1.3

  27. 2016

  28. 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

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

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

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

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

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

  34. 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.
  35. 2015

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

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

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

  39. 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.
  40. 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.
  41. 2014

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

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

  44. 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.
  45. S. Engblom, J. Pender: Approximations for the moments of nonstationary and state dependent birth-death queues

  46. 2013

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

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

  49. 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.
  50. 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.
  51. 2012

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

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

  54. 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

  55. 2011

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

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

  58. Until 2010

  59. (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.
  60. (2009) S. Engblom, L. Ferm, A. Hellander, P. Lötstedt: Simulation of Stochastic Reaction-Diffusion Processes on Unstructured Meshes

  61. (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)
  62. (2009) S. Engblom: Galerkin Spectral Method applied to the Chemical Master Equation

  63. (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.
  64. (2008) S. Engblom, PhD-thesis: Numerical Solution Methods in Stochastic Chemical Kinetics

  65. (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.
  66. (2008) S. Engblom, J. Cullhed, A. Hellander: The URDME Manual version 1.0

  67. (2006) S. Engblom, Licentiate-thesis: Numerical methods for the chemical master equation

  68. (2006) S. Engblom: Computing the Moments of High Dimensional Solutions of the Master Equation

  69. (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

Talks

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