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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Our approach

Our projects often combine approaches from scientific computing, machine learning, cloud computing and data engineering. For example, by integrating discrete event simulation, model-based optimization and human-in-the loop semi-supervised learning, we develop next-generation cloud native software for model inference and model exploration  in the StochSS project. Another example of this approach is found in the HASTE project, where we seek to develop intelligent cloud services supporting a new model for the management of massive image datasets from microscopy, based on intelligent information hierarchies.

Computational Systems Biology

A prominent application area in the lab is computational systems biology, where we develop new multiscale model to simulate gene regulatory networks with a spatial level of resolutions, as well as develop new approaches to model inference and model exploration.