Accelerating computationally-driven scientific discovery

From stochastic modeling of natural phenomena to intelligent cloud services for parameter space exploration and model inference, we find new ways to support scientific discovery by integrating large-scale simulation, scalable data analysis and artificial intelligence.

Modeling & stochastic simulation

A core approach is to model and simulate complex systems using stochastic descriptions. Stochastic chemical kinetics, agent-based models and Kinetic Monte Carlo are specialities.

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Machine Learning & Optimization

Applied machine learning and optimization are at the core of our toolbox for constructing intelligent scientific software to probe natural phenomena, and to develop models from data.

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Cloud computing & data engineering

Our research range from development of new ways to manage large and fast data to cloud native solutions for highly scalable interactive simulation workflows.

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Computational Systems Biology

A prominent application area in the lab is computational systems biology. Systems biology is an interdiciplinary field where mathematical modling, simulation and advanced analytics are combined with cell biology to model and understand for example gene regulation on a system level. This is challenging from a simulation point of view since models often need to take into account molecular interactions and movement on a subcellular scale as well as cell-cell interactions between millions of cells. We develop both multiscale models to simulate such systems, and new smart methods and software that bridge simulation and artificial intelligence to disentangle the complex interactions that lead to qualitatively .


Congratulations to @FredrikWrede who gives his half-time seminar today @uppsalauni! Great example of using #MachineLearning to do science. #modelexploration #stochss

We have an opening for a postdoc in privacy-preserving federated machine learning. Come join us @UU_University and develop foundational technology for sustainable #AI. #machinelearning #privacy #FedML

New paper on multiscale simulation of EGFR overexpression in Tumourigenesis. Stay tuned for more work by talented postdoc Anass Bouchnita 🙂 As usually a pleasure to publish in BMB!

Good news, we have gotten funding for working on new methods for model development and model exploration to be added to StochSS!

Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning #biorxiv_sysbio

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