Integrative Scalable Computing Lab

From smart e-infrastructure to highly scalable scientific software to privacy-preserving machine learning, our research seeks new, interdisciplinary ways of conducting large-scale computational investigation and to create models from data.

Mathematical modeling & 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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Artificial Intelligence

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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Smart systems for computationally-driven scientific discovery

Traditional, disciplinary approaches to computational study of natural systems often fail when data becomes very big, fast or complex, when models become high-dimensional and stochastic, and when models need to bridge many disparate scales.

To overcome these classical but elusive challenges in computational science, we believe that is is necessary to bridge disciplines and to take an holistic end-to-end focus from e-infrastructure to algorithm to end-user. This approach guides all that we do in the group, from solving concrete problems to recruitment strategy.

An integrative approach

Many of our projects combine methodology from different areas of computational mathematics and computer science. By collaborating across specializations we often find new ways of attacking challenging problems that are hard to handle using more traditional approaches. We leverage our expertise in data engineering and artificial intelligence to develop massively scalable computational experiments in e.g. systems biology. But we also leverage mathematical modeling, simulation and artificial intelligence to develop the actual e-infrastructure itself.


We have openings for a number of MSc students to work in the lab next semester. Topics range from stochastic simulation of multicellular systems to privacy-preserving Federated Learning. Reach out if you are interested! #FedML, #simulation, #thesisproject

Open position in my research group: PhD position in cell biology with an interest in automation. Join our team to build up an intelligent, automated laboratory for cell profiling, with applications in drug discovery:

"Apache Spark Streaming, Kafka and HarmonicIO: A Performance Benchmark and Architecture Comparison for Enterprise and Scientific Computing" will present the processing capabilities of different streaming frameworks at Bench19

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

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