- Professor in Scientific Computing
- Office Phone
- +46 18 471 27 54
- +46 18 51 19 25
- Mailing Address
- Scientific Computing
SE-751 05 Uppsala
- Visiting Address
The Ångström Laboratory, house 10
Find me at the Mathematics Genealogy Project...
...at the Research Gate...
...at Google Scholar...
...coordinating the TDB Seminar Series...
...the current faculty advisor
of UPPSALA SIAM
...a member of the board of Consor...
On the national side I was previously an elected member of
the Young Academy of
Sweden for the period 2016--2021. See my
Latest news and upcoming activities
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.
In case you are interested in doing a project work or a
MSc-thesis, please feel invited to contact me for further
Read more (including publications and talks).
Open project proposals:
driven by Data
In this line of research we are interested in the trade-off between
data on the one hand, and detailed modeling on the other hand. The
application focus is in epidemics and concerns modeling under
uncertainties and supporting risk-based decisions. Part of the work
will be done in SimInf.
details can be
Computational cell population
models: multiscale and multiphysics modeling
There are several openings for interesting projects within the
software framework URDME.
Suggestions include high-performance software development, advanced
new modeling, and improving simulation efficiency and flexibility, as
well as machine learning and data mining techniques.
can be found here.
Spring 2022: Numerical Functional Analysis (7.5hp, graduate
Spring 2022: Scientific Computing III 1TD397 (5.0hp).
Fall 2021: Scientific Computing III 1TD397 (5.0hp).
Spring 2020: Scientific Computing I 1TD393 (5.0hp).
Spring 2020: Numerical methods in stochastic modeling and simulations (7.5hp, graduate
Fall 2019: Scientific Computing II 1TD395 (5.0hp).
Previously also given 2018, 2017, 2016, 2015.
Spring 2019: Numerical Functional Analysis (7.5hp, graduate
Fall 2017:Advanced Numerical
Methods 1TD050 (10.hp)
Fall 2016: Advanced Numerical
Methods 1TD050 (10.hp)
Spring 2016: Numerical methods in stochastic modeling and simulations (7.5hp, graduate
Fall 2014: Numerical Functional Analysis (5.0hp, graduate
Spring 2014: Finite element methods II 1TD254 (5.0hp).
Fall 2012: Finite element methods 1TD253 (5.0hp).
Spring 2012: Classic Articles in Numerical Analysis (7.5hp, graduate course).
Fall 2010: Finite element methods 1TD253 (5.0hp).
- Please feel free to and use my software
(Matlab/C/Mex) under a very liberal license.
The software provided here includes a fairly wide range
of subroutines useful in research, computations and for
Please mail comments, suggestions, references,
criticism. Please do not send support questions.
- If a picture tells more than a thousand words
then a GIF-animation should tell at least a
SimInf and Data-driven epidemiological simulations:
URDME simulating stochastic spatial kinetics:
Surfactant in two-phase fluid flow (simulations by Minh Do-Quang):
Fibers in Stokes flow:
The master equation:
Autotuning (simulation by Marcus Holm):
Flow around airfoils (simulations by Paul Deglaire):
I am (sometimes!) involved in teaching Tango at Tangogruppen