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 .


Come help us develop algorithms and software so that we do not need to choose between privacy and advanced machine learning. #FedML #privacy #DataScience #Optimization @UU_University

Is your (academic) code bad? Build flags tied to a single platform? Hard-coded params that requires rebuild to change? Still, GET IT OUT THERE.

Present your research at SIAM’s first Conference on Mathematics of Data Science (MDS20)! This conference is being held May 5-7, 2020, in Cincinnati, Ohio and the deadline for minisymposium proposals is October 7th. Submit via the link below! #SIAMMDS20

Spending the fall semester working remote from Loughborough, England. Tips from my UK colleague of must-sees in or near East Midlands, both leisure and science, are most welcome πŸ™‚

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