Federated Machine Learning
Artificial intelligence is rapidly transforming our society. Machine learning models will be in every digital system we use, and it is imperative that we protect the integrity of data owners ...
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Likelihood-free parameter inference for gene regulatory networks
Biochemical reaction networks represent complex cellular regulatory mechanisms. These networks are typically analyzed using discrete stochastic simulation models. The models may involve numerous reactions involving a large number of chemical ...
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Hierarchical Analysis of Spatial and Temporal Data
The HASTE project, a SSF-funded project on computational science and big data, takes a holistic approach to new, intelligent ways of processing and managing very large amounts of microscopy images ...
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Scalable simulation of stochastic multicellular systems
In multicellular systems, cells of different types interact in various ways, both mechanically and chemically, to regulate complex processes. There is a large computational gap between detailed models of sub-cellular, ...
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Model exploration using active learning
The exploration of a system described by a non-linear, high-dimensional and stochastic computational model is a fundamental problem in all scientific disciplines relying on modeling and simulation.  In this project ...
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StochSS: Stochastic Simulation Service
StochSS is an integrated development environment (IDE) for discrete stochastic biochemical simulations. Users make use of a graphical user interface (GUI) to define their problem, including its domain (geometry, volume), ...
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Multiscale simulations of chemical kinetics
Life spans in size from small organisms consisting of single cells to complex organisms built up of billions of cells. Even the single-cell organisms are challenging to fully understand and ...
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