Anass Bouchnita joins the lab

A new semester has just started, and there is no better way to kick it off than to welcome a new group member! Anass Bouchnita has joined us for to do a postdoc in computational systems biology. He will work on multiscale methods for multicellular modeling and simulation, in a collaboration with Igor Adameyko at Karolinska Institute and Vienna Medical University in which we model systems in development driven by cell-lineage tracking data. The position is largely funded by the eSSENCE collaboration on eScience.

Anass Bouchnita received his Engineer’s and PhD degree in Modelling and Scientific Computing from the Mohammadia School of Engineering. He also obtained a PhD degree from Claude Bernard Lyon 1 University. He works on the development of novel mathematical models that simulate complex physiological systems. His research interests include reaction-diffusion systems, multiscale cell-based models, PK-PD modelling, and their various applications in biomedicine.

Welcome Anass!

“Ten simple rules” for establishing a national scale OpenStack cloud e-infrastructure for science

The SNIC Science Cloud (SSC) team has published a paper in the 2017 conference on IEEE eScience.  SNIC Science cloud has been an infrastructure project run by the Swedish National Infrastructure for Computing (SNIC) with the purpose to assess if and how SNIC should offer cloud infrastructure to the scientific community. The project is now coming to an end, and the SSC OpenStack resources will become a part of the SNIC production infrastructure landscape. In the paper, we summarize the experiences gained in the project and provide recommendations in the form of “ten simple rules”  for how to successfully establish a science cloud community cloud infrastructure on a national scale. Read the paper here:

http://ieeexplore.ieee.org/document/8109140/

Mesoscopic-microscopic hybrid algorithm with automatic partitioning

We have developed a multiscale method coupling the mesoscopic and microscopic scales. On the mesoscopic scale, systems are modeled as discrete jump processes on a structured or unstructured grid, while on the microscopic scale, molecules are modeled by hard spheres diffusing in continuous space.

Microscopic simulations are accurate but computationally expensive. In this paper we try to automatically detect which parts of a system that need high accuracy to be accurately resolved, and which parts can be simulated on the coarser mesoscopic scale. We also extend a previously developed hybrid algorithm (http://epubs.siam.org/doi/abs/10.1137/110832148), to improve its convergence properties.

This new algorithm makes it possible to simulate larger systems with greater accuracy than before, thus significantly widening the scope of problems that can be simulated at the particle level.

The manuscript has been submitted and is under review. It is available on Arxiv at https://arxiv.org/abs/1709.00475

Stefan Hellander joins the lab

 

We are delighted to have Stefan Hellander join the lab!  Stefan obtained his Ph.D. in scientific computing from Uppsala University in 2013, with the thesis “Stochastic Simulation of Reaction-Diffusion Processes”, advised by Prof. Em. Per Lötstedt. He then went on to work as a postdoc in the lab of Prof. Linda Petzold at UCSB, until returning to Uppsala in August of 2017. His research interests include microscopic and mesoscopic modeling and simulation of reaction-diffusion systems, as well as multiscale modeling with the aim of accurately integrating the different scales.

HASTE is granted 29 MSEK funding from SSF

Our project Hierarchical Analysis of Spatial and TEmporal Data (HASTE) is granted 29 MSEK funding from SSF. The project, with PI Carolina Wählby  and co-PIs Andreas Hellander, Ola Spjuth and Mats Nilsson, will explore new ways to gain insight from massive amounts of spatial and temoral image data through hierarchical analysis models and smart cloud systems for managing data, see http://haste.research.it.uu.se.

Postdoc in Scientific Computing/Applied Cloud Computing

We are looking for a talented individual to join our efforts on creating smart and scalable cloud services to support simulation-driven scientific discovery via large-scale computational experiments such as parameter sweeps. This is a classic and very important problem that we will approach in new ways, leveraging recent advances in cloud computing, data-intensive computing and machine learning. See the full advertisement here (Deadline Sept. 1):

http://www.uu.se/en/about-uu/join-us/details/?positionId=107117

The successful candidate will contribute to the interdisciplinary research group Distributed Computing Applications (DCA) at the Department of Information Technology, Uppsala University.