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.

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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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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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.

News

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: https://pharmb.io/blog/phdposition2019-cellbio/

"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 http://www.benchcouncil.org/bench19/index.html https://arxiv.org/abs/1807.07724

Come help us develop algorithms and software so that we do not need to choose between privacy and advanced machine learning. https://uu.se/en/about-uu/join-us/details/?positionId=284651 #FedML #privacy #DataScience #Optimization @UU_University

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