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.
I am the main supervisor for 2 PhD-students:
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
Eriksson (started 2017). Robins project is Computational
Modeling, Parameterization, and Evaluation of the spread of
Diseases and is joint with Stefan Widgren using
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.
Find me at the Mathematics Genealogy Project...
Parametrisation of In Silico Tumour Models by Jonas Radvilas
Umaras (2018, MSc Computational Science).
modeling of avascular tumours using a hybrid on-lattice framework
for cell-population dynamics by Lina Viklund (2018, MSc
modeling of interactions between colonic crypts by Martin Edin
and Nils Erlanson (2017, BSc Engineering Physics)
Stochastic Neuron Modeling: with applications in deep brain
stimulation by Aleksandar Senek (2017, MSc Engineering
- Bayesian Parameterization in the spread of Diseases by Robin Eriksson (2017, MSc Engineering Physics)
Stochastic Morphogenesis by Yakup Saygun (2015, MSc Engineering
Performance Computing aspects of Single Particle Machine Learning
by Marcus Näslund (2015, MSc Computational Science)
- Efficient Parameter Inference for Stochastic Chemical
Kinetics by Debdas Paul (2014, MSc Theoretical Biological
Physics/Computational Systems Biology)
- Towards mesoscopic modeling of firing neurons: a feasibility
study by Emil Berwald (2014, MSc Engineering Physics)
- GPU-Parallel simulation of rigid fibers in Stokes
flow by Ronny Eriksson (2014, BSc Computer Science)
- Parallelization and performance in simulation of disease spread by animal transfer by Fredrik Pasanen and Magnus Söderling (2012, BSc Computer Science)
...at the Research Gate...
...at Google Scholar...
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
⇒ 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
Markov chain and the equation governing the probability density is
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
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.
I was also involved in a project joint
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,
Goude. The work has resulted in a two-dimensional random vortex
method simulating fluid flows around general airfoils at a quite
number. My contribution has been focused around a user-friendly
and very efficient implementation of
See also: DiVA records for Author:
Publication list (pdf)
J. Liu, S. Engblom, C. Nettelblad: Flash X-ray diffraction imaging in 3D: a proposed analysis pipeline
S. Engblom, R. Eriksson, and S. Widgren: Bayesian epidemiological modeling over high-resolution network data
S. Widgren, T. Rosendal, S. Engblom, and K. Ståhl: SimInf for
spatio-temporal data-driven modeling of African swine fever in
- Accepted in GeoVet 2019. Novel spatio-temporal
approaches in the era of Big Data (url)
S. Widgren, P. Bauer, R. Eriksson, and S. Engblom: SimInf: An
R package for data-driven stochastic disease spread
- Accepted for publication in J. Stat. Softw. (2019).
Available via arXiv.
J. Lindén, P. Bauer, S. Engblom, and B. Jonsson: Exposing
inter-process information for efficient PDES of spatial
stochastic systems on multicores
J. Liu, G. Schot, S. Engblom: Supervised Classification
Methods for Flash X-ray single particle diffraction Imaging
D. Arjmand, S. Engblom, G. Kreiss: Temporal upscaling in micro magnetism via heterogeneous multiscale methods
S. Engblom, P. Lötstedt, L. Meinecke: Mesoscopic Modeling of Random Walk and Reactions in Crowded Media
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
S. Engblom, D. Wilson, R. Baker: Scalable population-level modeling of biological cells incorporating mechanics and kinetics in continuous time
P. Bauer, S. Engblom, S. Mikulovic, and A. Senek: Multiscale modeling via split-step methods in neural firing
J. Liu, S. Engblom, and C. Nettelblad: Assessing Uncertainties in X-ray Single-particle Three-dimensional reconstructions
S. Engblom: Stochastic simulation of pattern
formation in growing tissue: a multilevel approach
A. Goude, S. Engblom: A general high order two-dimensional panel method
A. Chevallier and S. Engblom: Pathwise error
bounds in multiscale variable splitting methods for spatial
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
- DiVA record.
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):
S. Engblom and S. Widgren: Data-driven computational disease spread modeling: from measurement to parametrisation and control
S. Engblom: Stability and Strong Convergence for Spatial Stochastic Kinetics
S. Engblom, A. Hellander, P. Lötstedt: Multiscale Simulation of Stochastic Reaction-Diffusion Networks
G. Christoffersson, J. Lomei, P. O'Callaghan, J. Kreuger, S. Engblom, and M. Phillipson: Vascular sprouts induce local attraction of proangiogenic neutrophils
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).
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):
P. Bauer, S. Engblom: The URDME Manual version 1.3
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
S. Engblom, D. Lukarski: Fast Matlab compatible sparse assembly on multicore computers
E. Blanc, S. Engblom, A. Hellander, P. Lötstedt: Mesoscopic modeling of stochastic reaction-diffusion kinetics in the subdiffusive regime
P. Bauer, S. Engblom, S. Widgren: Fast event-based epidemiological simulations on national scales
L. Meinecke, S. Engblom, A. Hellander, and P. Lötstedt: Analysis and design of jump coefficients in discrete stochastic diffusion models
S. Engblom and V. Sunkara: Preconditioned Metropolis sampling as a strategy to improve efficiency in posterior exploration
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).
A. Milias-Argeitis, S. Engblom, P. Bauer, M. Khammash: Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks
S. Engblom: Strong convergence for split-step methods in stochastic jump kinetics
T. Ekeberg, S. Engblom, J. Liu: Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters
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).
P. Bauer, S. Engblom: Sensitivity estimation
and inverse problems in spatial stochastic models of chemical
In Numerical Mathematics and Advanced Applications: ENUMATH 2013, Lecture Notes in Computational Science and Engineering 103, 519--527 (2015): (doi).
S. Engblom: On the stability of stochastic jump kinetics
M. Holm, S. Engblom, A. Goude, S. Holmgren: Dynamic
autotuning of adaptive fast multipole methods on hybrid multicore CPU
& GPU systems
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).
S. Engblom, J. Pender: Approximations for the moments of nonstationary and state dependent birth-death queues
A. Goude and S. Engblom: Adaptive fast multipole methods on the GPU
S. Engblom, M. Do-Quang, G. Amberg, A-K. Tornberg: On diffuse interface modeling and simulation of surfactants in two-phase fluid flow
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).
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
B. Drawert, S. Engblom, and A. Hellander: URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries
P. Bauer, B. Drawert, S. Engblom, A. Hellander: URDME v. 1.2: User's manual
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
S. Engblom: On well-separated sets and fast multipole methods
B. Drawert, S. Engblom, A. Hellander: URDME v. 1.1: User's manual
(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.
- Review: MR2575044
Preprint at arXiv.
(2009) S. Engblom, L. Ferm, A. Hellander, P. Lötstedt: Simulation of Stochastic Reaction-Diffusion Processes on Unstructured Meshes
(2009) S. Engblom: Spectral Approximation of Solutions to
the Chemical Master Equation
J. Comput. Appl. Math. 229(1):208--221, 2009: (doi)
- Review: MR2522514 (Unfortunately, this review is not accurate)
(2009) S. Engblom: Galerkin Spectral Method applied to the Chemical Master Equation
(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!
(2008) S. Engblom, PhD-thesis: Numerical Solution Methods in Stochastic Chemical Kinetics
(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)
(2008) S. Engblom, J. Cullhed, A. Hellander: The URDME Manual version 1.0
(2006) S. Engblom, Licentiate-thesis: Numerical methods for the chemical master equation
(2006) S. Engblom: Computing the Moments of High Dimensional Solutions of the Master Equation
(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.
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