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
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.
I am currently the main supervisor for 3 PhD-students:
I was the main advisor of
I am also the secondary advisor
Andersson. I was previously the secondary advisor of
- Jonas Evaeus (started 2021). Jonas project is Identification and prediction in Epidemiological models.
- Erik Blom
(started 2021). Eriks project is Scalable computational modeling of
- Gesina Menz
(started 2022). Gesinas project is Data-driven modeling of living
Find me at the Mathematics Genealogy Project...
Bayesian Computation for Data-Driven Epidemiological Models by
Christoph Nötzli (2023, MSc Data Science)
the Estimation of the infection rate and the fraction of infections
leading to death in epidemiological simulation by Jakob
Gölén (2023, MSc Engineering Physics)
in grid-based time-continuous cell population models by Joel
Olofsson (2022, MSc Engineering Physics)
modelling of quorum sensing using cascade delay by Nils Axelsson
and David Mårsäter (2022, BSc Engineering Physics)
med artificiell evolution: Warburgeffekten och cancercellers
metabolism by David Näsström and Marcus Medhage (2022, BSc
Hybrid Modeling of Avascular Tumours by Erik Blom (2021,
MSc Computational Science)
multithreading for a fast multipole method using OpenMP by
Ludwig Ridderstolpe (2021, BSc Computer Science)
of Adaptive Fast Multipole Method in three dimensions for
time-dependent problems by Zain Nawas (2021, MSc
Priority Queues with support for priority updates at arbitrary
indexes by Erik Granberg (2021, BSc Computer Science)
- Heterogeneous Multiscale Method in Markovian event-based models --
With applications in tumor modeling by An Khang Bui (2020, MSc
Numerische Mechanik, Technical University of Munich)
parallel implementation of spatially distributed stochastic
chemical kinetics by Pontus Melin (2020, BSc Computer
complexity of the Fibonacci heap in a simulation and modelling
framework by Elwira Johansson (2020, BSc Computer
- Bayesian inference in Epidemics: consistency and convergence by
Samuel Bronstein (2019, MSc (eq.), Applied Mathematics, ENS
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 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
⇒ 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)
, available via arXiv.
E. Blom, S. Engblom: Morphological stability for in silico
models of avascular tumors
R. Marin, H. Runvik, A. Medvedev, and S. Engblom: Bayesian Monitoring of COVID-19 in
- In Epidemics, 45 (2023) (doi).
Available via arXiv.
- Reproduce the numerical experiments: download from GitHub.
S. Bronstein, S. Engblom, and R. Marin: Bayesian inference in
Epidemics: linear noise analysis
- In Math. Biosci. Eng., 20(2) (2023):4128--4152 (doi).
Available via arXiv.
- Reproduce the numerical experiments: BISDE.tar (requires Matlab, R,
and relies on URDME and SimInf).
B. Kennedy, H. Fitipaldi, U. Hammar, et al.: App-based COVID-
19 syndromic surveillance and prediction of hospital admissions
in COVID Symptom Study Sweden
- In Nature Commun., 13(2110) (2022) (doi).
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
J. Bull and S. Engblom: Distributed and
Adaptive Fast Multipole Method In Three Dimensions
S. Engblom, R. Eriksson, P. Vilanova: Towards Confident
Bayesian Parameter Estimation in Stochastic Chemical
In Numerical Mathematics and Advanced Applications: ENUMATH
2019, Lecture Notes in Computational Science and Engineering
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
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
S. Engblom et al.: The URDME Manual version 1.4
S. Widgren, P. Bauer, R. Eriksson, and S. Engblom: SimInf: An
R package for data-driven stochastic disease spread
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
S. Engblom: Stochastic simulation of pattern
formation in growing tissue: a multilevel approach
D. Arjmand, S. Engblom, G. Kreiss: Temporal upscaling in micro magnetism via heterogeneous multiscale methods
S. Widgren, T. Rosendal, S. Engblom, and K. Ståhl: SimInf for
spatio-temporal data-driven modeling of African swine fever in
- In GeoVet 2019: Novel spatio-temporal approaches in the era
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
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.
(problems with viewing pdf-files can usually be solved using "Save Link