Stefan Engblom, TDB, Uppsala universitet

Uppsala universitet

Stefan Engblom

Short CV: (pdf)

Publications: (pdf)

	Information Technology : Scientific Computing Stefan Engblom
	Research 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. 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 Robin Marin who successfully defended his thesis Computational Modeling, Parameterization, and Evaluation of the Spread of Diseases (2022). Jing Liu who successfully defended her thesis Towards Fast and Robust Algorithms in Flash X-ray single-particle Imaging (2020). This project was run joint with Janos Hajdu's group at the Department of Cell and Molecular Biology. Pavol Bauer who successfully defended his thesis Parallelism in Event-Based Computations with Applications in Biology (2017). I am also the secondary advisor for Anna Frigge, Alfred Andersson, and Helena Andersson. I was previously the secondary advisor of Adrien Coulier. PhD thesis Multiscale Modeling in Systems Biology: Methods and Perspectives (2021). Afshin Zafari. PhD thesis Advances in Task-Based Parallel Programming for Distributed Memory Architectures (2018). Stefan Widgren. PhD thesis Studies on verotoxigenic Escherichia coli O157 in Swedish cattle. From sampling to disease spread modelling (2016). Lina Meinecke. PhD thesis Stochastic Simulation of Multiscale Reaction-Diffusion Models via First Exit Times (2016). Marcus Holm. Licentitate thesis Scientific computing on hybrid architectures (2013). MSc/BSc-theses: Approximate Bayesian Computation for Data-Driven Epidemiological Models by Christoph Nötzli (2023, MSc Data Science) Investigating the Estimation of the infection rate and the fraction of infections leading to death in epidemiological simulation by Jakob Gölén (2023, MSc Engineering Physics) Cell-sorting in grid-based time-continuous cell population models by Joel Olofsson (2022, MSc Engineering Physics) Computational modelling of quorum sensing using cascade delay by Nils Axelsson and David Mårsäter (2022, BSc Engineering Physics) Tumörspridning med artificiell evolution: Warburgeffekten och cancercellers metabolism by David Näsström and Marcus Medhage (2022, BSc Engineering Physics) Towards Hybrid Modeling of Avascular Tumours by Erik Blom (2021, MSc Computational Science) Implementing multithreading for a fast multipole method using OpenMP by Ludwig Ridderstolpe (2021, BSc Computer Science) Performance of Adaptive Fast Multipole Method in three dimensions for time-dependent problems by Zain Nawas (2021, MSc Computational Science) Comparing 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) A parallel implementation of spatially distributed stochastic chemical kinetics by Pontus Melin (2020, BSc Computer Science) Practical complexity of the Fibonacci heap in a simulation and modelling framework by Elwira Johansson (2020, BSc Computer Science) Bayesian inference in Epidemics: consistency and convergence by Samuel Bronstein (2019, MSc (eq.), Applied Mathematics, ENS Paris) Bayesian Parametrisation of In Silico Tumour Models by Jonas Radvilas Umaras (2018, MSc Computational Science). Computational modeling of avascular tumours using a hybrid on-lattice framework for cell-population dynamics by Lina Viklund (2018, MSc Engineering Physics). Mathematical modeling of interactions between colonic crypts by Martin Edin and Nils Erlanson (2017, BSc Engineering Physics) Multiscale Stochastic Neuron Modeling: with applications in deep brain stimulation by Aleksandar Senek (2017, MSc Engineering Physics) Bayesian Parameterization in the spread of Diseases by Robin Eriksson (2017, MSc Engineering Physics) Computational Stochastic Morphogenesis by Yakup Saygun (2015, MSc Engineering Physics) High 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) Find me at the Mathematics Genealogy Project... ...at the Research Gate... ...at Google Scholar... ...at ORCID.... ...at ResearcherID .... ...at arXiv... ...at GitHub. Previously 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. 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) E. Blom, S. Engblom: Morphological stability for in silico models of avascular tumors In Bull. Math. Biol, 86 (2024) (doi). Available via arXiv. E. Blom, S. Engblom, G. Menz: Modeling the hallmarks of avascular tumors Available via arXiv. 2023 R. Marin, H. Runvik, A. Medvedev, and S. Engblom: Bayesian Monitoring of COVID-19 in Sweden In Epidemics, 45 (2023) (doi). Available via arXiv. Reproduce the numerical experiments: download from GitHub. DiVA record. 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). DiVA record. 2022 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). DiVA record. 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 At the 2022 International Joint Conference on Neural Networks (IJCNN) at IEEE WCCI 2022 (doi). Preprint at arXiv. DiVA record. 2021 J. Bull and S. Engblom: Distributed and Adaptive Fast Multipole Method In Three Dimensions In Commun. Comput. Phys. 30(4):959--984 (2021): (doi) Available via arXiv. DiVA record. Download the daFMM3D software at www.stenglib.org. 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. 2020 J. Liu, S. Engblom, C. Nettelblad: Flash X-ray diffraction imaging in 3D: a proposed analysis pipeline In J. Opt. Soc. Amer. A 37(10):1673--1686 (2020) (doi). DiVA record. Available via arXiv. S. Engblom, R. Eriksson, and S. Widgren: Bayesian epidemiological modeling over high-resolution network data In Epidemics 32 (2020) (doi). Available via arXiv. DiVA record. 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. S. Engblom et al.: The URDME Manual version 1.4 Available via arXiv. Download URDME 1.4 at www.urdme.org. 2019 S. Widgren, P. Bauer, R. Eriksson, and S. Engblom: SimInf: An R package for data-driven stochastic disease spread simulations In J. Stat. Softw., 91(12):1--42 (2019) (doi). DiVA record. Available via arXiv. Download the latest version of SimInf at www.siminf.org. 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. J. Liu, G. Schot, S. Engblom: Supervised Classification Methods for Flash X-ray single particle diffraction Imaging In Opt. Express, 27(4):3884--3899 (2019) (doi). DiVA record. Available via arXiv. S. Engblom: Stochastic simulation of pattern formation in growing tissue: a multilevel approach In Bull. Math. Biol., 81:3010--3023 (2019) (doi). DiVA record. Available via arXiv. D. Arjmand, S. Engblom, G. Kreiss: Temporal upscaling in micro magnetism via heterogeneous multiscale methods In J. Comput. Appl. Math. 345:99--113 (2019) (doi). DiVA record. Available via arXiv. 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) 2018 S. Engblom, P. Lötstedt, L. Meinecke: Mesoscopic Modeling of Random Walk and Reactions in Crowded Media In Phys. Rev. E 98(3):033304 (2018): (doi). DiVA record. Note: illustration seclected for the Phys. Rev. E Kaleidoscope in September 2018! Available via arXiv. 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* In Veterinary Res. 49(78), (2018): (doi). DiVA record. S. Engblom, D. Wilson, R. Baker: Scalable population-level modeling of biological cells incorporating mechanics and kinetics in continuous time In Roy. Soc. Open Sci. 5(8), (2018): (doi). DiVA record. Available via arXiv. P. Bauer, S. Engblom, S. Mikulovic, and A. Senek: Multiscale modeling via split-step methods in neural firing In Math. Comput. Model. Dyn. Syst. 24(4):409--425, (2018): (doi). DiVA record. Available via arXiv. J. Liu, S. Engblom, and C. Nettelblad: Assessing Uncertainties in X-ray Single-particle Three-dimensional reconstructions In Phys. Rev. E. 98(1):013303, (2018): (doi). DiVA record. Note: illustration seclected for the Phys. Rev. E Kaleidoscope in July 2018! Preprint via arXiv. A. Goude, S. Engblom: A general high order two-dimensional panel method In Appl. Math. Model. 60:1--17 (2018): (doi). DiVA record. Available via arXiv. A. Chevallier and S. Engblom: Pathwise error bounds in multiscale variable splitting methods for spatial stochastic kinetics In SIAM J. Numer. Anal. 56(1):469--498, (2018) (doi). DiVA record. Available via arXiv. 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. 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. 2017 S. Engblom and S. Widgren: Data-driven computational disease spread modeling: from measurement to parametrisation and control Chapter 11 in Disease Modelling and Public Health, Part A, Volume 36 of Handbook of Statistics, (doi). DiVA record. S. Engblom: Stability and Strong Convergence for Spatial Stochastic Kinetics Chapter 3.3 in Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology, (doi). DiVA record. S. Engblom, A. Hellander, P. Lötstedt: Multiscale Simulation of Stochastic Reaction-Diffusion Networks Chapter 2.4 in Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology, (doi). DiVA record. G. Christoffersson, J. Lomei, P. O'Callaghan, J. Kreuger, S. Engblom, and M. Phillipson: Vascular sprouts induce local attraction of proangiogenic neutrophils In J. Leukocyte Biol. 102(3):741--751, (2017): (doi). DiVA record. 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. 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. P. Bauer, S. Engblom: The URDME Manual version 1.3 Technical report: (abstract), (pdf) Available via arXiv. Download URDME 1.3 at www.urdme.org. 2016 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 In Veterinary Res. 47:81(1):1--17, (2016). (doi). DiVA record. S. Engblom, D. Lukarski: Fast Matlab compatible sparse assembly on multicore computers In Parallel Comput. 56:1--17 (2016) (doi). Preprint via arXiv. DiVA record. E. Blanc, S. Engblom, A. Hellander, P. Lötstedt: Mesoscopic modeling of stochastic reaction-diffusion kinetics in the subdiffusive regime In Multiscale Model. Simul. 14(2):668--707 (2016): (doi). Preprint via arXiv. DiVA record. P. Bauer, S. Engblom, S. Widgren: Fast event-based epidemiological simulations on national scales In Int. J. High Perf. Comput. Appl. 30(4):438--453 (2016): (doi). Preprint via arXiv. DiVA record. L. Meinecke, S. Engblom, A. Hellander, and P. Lötstedt: Analysis and design of jump coefficients in discrete stochastic diffusion models In SIAM J. Sci. Comput. 38(1):A55--A83 (2016): (doi). Preprint via arXiv. DiVA record. S. Engblom and V. Sunkara: Preconditioned Metropolis sampling as a strategy to improve efficiency in posterior exploration In IFAC-PapersOnLine 49(26):89--94 (2016): (doi). DiVA record. 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. 2015 A. Milias-Argeitis, S. Engblom, P. Bauer, M. Khammash: Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks In J. R. Soc. Interface 12(113):1--10 (2015): (doi). Preprint via arXiv. DiVA record. S. Engblom: Strong convergence for split-step methods in stochastic jump kinetics In SIAM J. Numer. Anal. 53(6):2655--2676 (2015): (doi) DiVA record. Review: MR3432851. Preprint via arXiv. T. Ekeberg, S. Engblom, J. Liu: Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters In Int. J. High Perf. Comput. Appl. 29(2):233--243 (2015): (doi) Available via arXiv. DiVA record. 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. 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. 2014 S. Engblom: On the stability of stochastic jump kinetics In Appl. Math. 5(19):3217--3239 (2014) (doi). DiVA record. Preprint via arXiv. Technical report: (abstract), (pdf). M. Holm, S. Engblom, A. Goude, S. Holmgren: Dynamic autotuning of adaptive fast multipole methods on hybrid multicore CPU & GPU systems In SIAM J. Sci. Comput. 36(4):C376--C399 (2014): (doi) DiVA record. Review: MR3253697. Preprint via arXiv. 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. S. Engblom, J. Pender: Approximations for the moments of nonstationary and state dependent birth-death queues Available via arXiv. DiVA record. 2013 A. Goude and S. Engblom: Adaptive fast multipole methods on the GPU In J. Supercomput 63(3):897--918, 2013: (doi) DiVA record. Preprint at arXiv. Technical report: (abstract), (pdf). S. Engblom, M. Do-Quang, G. Amberg, A-K. Tornberg: On diffuse interface modeling and simulation of surfactants in two-phase fluid flow In Commun. Comput. Phys. 14(4):879--915, 2013: (doi). DiVA record. Preprint at arXiv. 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. 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. 2012 B. Drawert, S. Engblom, and A. Hellander: URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries In BMC Syst. Biol. 6(76):1--17, 2012: (doi). DiVA record. P. Bauer, B. Drawert, S. Engblom, A. Hellander: URDME v. 1.2: User's manual Technical report: (abstract), (pdf) Also available via arXiv. Download URDME 1.2 from www.urdme.org. 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 Preprint at arXiv. 2011 S. Engblom: On well-separated sets and fast multipole methods In Appl. Numer. Math. 61(10):1096--1102, 2011: (doi) DiVA record. Review: MR2820968 Preprint at arXiv. B. Drawert, S. Engblom, A. Hellander: URDME v. 1.1: User's manual Technical report: (abstract), (pdf) Also available via arXiv. Download URDME 1.1 from www.urdme.org. Before 2010 (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. (2009)** S. Engblom, L. Ferm, A. Hellander, P. Lötstedt: Simulation of Stochastic Reaction-Diffusion Processes on Unstructured Meshes In SIAM J. Sci. Comput. 31(3):1774--1797, 2009: (doi). Note: as of May/June 2018 this item is a Web of Science Highly cited paper. DiVA record. Review: MR2491545 (2010e:60142) Technical report: (abstract), (pdf) Preprint at arXiv. (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) (2009) S. Engblom: Galerkin Spectral Method applied to the Chemical Master Equation In Commun. Comput. Phys. 5(5):871--896, 2009: (abstract), (pdf) DiVA record. (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. (2008) S. Engblom, PhD-thesis: Numerical Solution Methods in Stochastic Chemical Kinetics DiVA record Fulltext (pdf) (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. (2008) S. Engblom, J. Cullhed, A. Hellander: The URDME Manual version 1.0 Technical report: (abstract), (pdf) Also available via arXiv. Download URDME 1.0 through the download-link. (2006) S. Engblom, Licentiate-thesis: Numerical methods for the chemical master equation DiVA record. (2006) S. Engblom: Computing the Moments of High Dimensional Solutions of the Master Equation In Appl. Math. Comput. 180(2):498--515, 2006: (doi) DiVA record. Technical report: (abstract), (pdf) (2006) S. Engblom: Gaussian quadratures with respect to discrete measures Technical report: (abstract), (pdf) DiVA record. 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. Talks (problems with viewing pdf-files can usually be solved using "Save Link As") Stochastic modeling for the single cell and the cell population: considerations for data-driven methodologies Invited talk given in the Systems Biology Seminar series. Host: Nicole Radde. (pdf). Bridging the single cell with the cell population: opening up for data-driven methodologies At the ENUMATH conference, September 30--October 4, 2019, at Egmond aan Zee, The Netherlands. (pdf). Bayesian epidemiologial modeling: with little and without data At the conference Multiscale Modelling of Materials and Molecules in Uppsala, June 3--5, 2019. (pdf). From the bottom and up: bridging the single cell with the cell population At the workshop Multidisciplinary and multiscale approaches to bridge the gap between molecular and cellular level, October 13--17, 2018, at the Centro di Ricerca Mathematica (CRM) Ennio De Giorgio in Pisa, Italy. The workshop was part of the Program Advanced asymptotics of PDEs, modeling and extreme statistics and their applications to data analysis in cell biology. (pdf). Bridging the scales between the single cell and the cell population - computational considerations At the Workshop IV: Uncertainty Quantification for Stochastic Systems and Applications, November 13--17, 2017, part of the Program Complex High-Dimensional Energy Landscapes at IPAM, UCLA, CA, USA. (pdf). See the talk here. A case study of Data-driven computational modeling in Epidemics: bringing the dirt to the classroom In the LTH Seminar Series, Lund University, March 6, Lund, Sweden, 2017. (pdf). Data-driven Epidemiological Simulations: Verotoxigenic E. coli O157 At the workshop Mathematical Biology for Understanding Emerging Infectious Diseases at the Human-Animal-Environment Interface: a "One Health" Approach, November 20--25, 2016 in Banff, Canada. (pdf). The lecture was video recorded (mp4). Related talks were given also in the workshop Scientific Computing in Sweden and in the Bayesian Meeting. Stability and strong convergence in multiscale methods for spatial stochastic kinetics At the Spatially Distributed Stochastic Dynamical Systems in Biology at the Isaac Newton Institute for mathematical Sciences, June 20--24, Cambridge, UK, 2016: (pdf). The lecture was video recorded (embedded video). Pathwise analysis for split-step methods and multiscale variable splitting in spatial stochastic kinetics At SciCADE 2015, in the minisymposia Multiscale and splitting methods: Theory and Applications, in Potsdam/Germany, September 14--18, 2015: (pdf). An updated version of this talk was given at KTH in the Numerical analysis series (2015): (pdf). Towards consistent and effective modeling in the stochastic reaction-diffusion framework In the Mathematical Biology and Ecology Seminar series at Oxford University, UK, May 15, 2015: (pdf). Pointless exams at advanced level courses At the internal pedagogical conference of the Faculty of Science and Technology, Uppsala, Sweden, 2015: (pdf). Minisymposium Frontiers in Cell Migration, December 12, 2014, Uppsala university, Uppsala, Sweden. Final program (pdf) Organizing committee: Stefan Engblom, Johan Kreuger, Mia Phillipson. Mesoscopic Stochastic Modeling: Diffusion Operators, Multiphysics Couplings, and Convergence In the BIRS workshop Particle-Based Stochastic Reaction-Diffusion Models in Biology in Banff, Alberta, Canada, November 2014: (pdf). The lecture was video recorded (mp4). Mesoscopic Stochastic Modeling and the Diffusion Operator In the minisymposium Position-jump models of biological processes on irregular lattices at the 9th European Conference on Mathematical and Theoretical Biology in Gothenburg, Sweden, June 2014: (pdf). Also organized at the same conference (with Stefan Widgren) the minisymposium Data-driven modeling of epidemics: program with abstracts. A new take at Adaptive Fast Multipole Methods: application, implementation, and hybrid CPU/GPU parallelism In the KCSE Seminar series at the KTH Computational Science and Engineering Centre in Stockholm, Sweden, November 2013: (pdf). Spatial Stochastic Modeling in URDME: consistency, software, and applications to neuronal processes (talk given by Pavol Bauer) At the INCF workshop, San Diego CA, USA, October 2013: (pdf). Sensitivity estimation and inverse problems in spatial stochastic models of chemical kinetics At the ENUMATH conference, EPFL, Switzerland, August 2013: (pdf). Stochastic Modeling in Reaction-Transport Processes: Forward and Backward considerations In the BSSE seminar series at ETH, Basel, Switzerland, March 2013: (pdf). A guest lecture entitled Mesoscopic Stochastic Modeling of Reaction-Transport Processes was also given (pdf). Biologi: en utmaning för Numerisk Analys Docent lecture, Uppsala, Sweden, March 2013: (pdf in swedish). Tips, tricks, and experiences with active grading criteria and pointless exams At Gustaf Söderlind's 60th Birthday Symposium in Lund, Sweden, December 2012: (pdf). Parallel Adaptive Fast Multipole Method: application, design, and more... At the Parallel Scientific Computing and Programming (PSCP) Colloquium in Uppsala, Sweden, March, 2011: (pdf). Simulation of surfactant in diffuse interface flow At the workshop FLASHES on research in Scientific Computing in Uppsala, Sweden, November 2010: (pdf). Mesoscopic Stochastic Modeling of Reaction-Diffusion Processes In the KCSE Seminar series at the KTH Computational Science and Engineering Centre in Stockholm, Sweden, February 2010: (pdf). Simulation of stochastic reaction-diffusion processes on unstructured meshes At the First Swedish meeting on Theory and Mathematics in Biology and Medicine, held at the Centre for Interdisciplinary Mathematics in Uppsala, Sweden, December 2009: (pdf). Numerical Solution Methods in Stochastic Chemical Kinetics PhD dissertation defense (pdf) succesfully presented the 28th of November 2008. Opponent: Wilhelm Huisinga. Grading committe: Jesper Oppelstrup, Stig Larsson and Lina von Sydow. Simulation of stochastic reaction-diffusion processes on unstructured meshes Given at the Fraunhofer-Chalmers Centre in Gothenburg, Sweden, October 2008: (pdf). Parallel Solution of Multiscale Stochastic Chemical Kinetics At the Dahlquist Fellowship Workshop at KTH, Stockholm, Sweden, October 2008: (pdf). The same talk was given at the ICNAAM conference at Kos, Greece, September 2008. Numeriska metoder för den kemiska masterekvationen Licentitate seminar (pdf in swedish) succesfully presented the 27th of September 2006. External reviewer: Anders Szepessy, KTH. Numerical solution methods for the master equation At the IHP-EU Network Workshop/Winter school 2005: "Breaking complexity" in Bad Honnef, Germany, December 2005: (pdf)

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