Omar Javed joins the group as a visiting PhD student

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We are happy to welcome Omar Javad for his exchange year as a visiting PhD student! Omar a PhD student in Università della Svizzera italiana (USI), Switzerland. His research interest includes dynamic program analysis, mining software repositories, virtualization and runtime verification. In particular I am investigating dynamic program analysis technique for understanding functional and non-functional…

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Applied Cloud Computing, News

Trusted Execution Environment (TEE) For Federated Learning

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The Scaleout team, in close collaboration with ISCL, was granted SEK 2.1M to explore the use of secure enclaves to increase the veracity in federated learning. Read more about the project here: https://www.scaleoutsystems.com/post/trusted-execution-environment-for-federated-learning

Data-Intensive Computing, FedML, Funding, News

Smart Resource Management for Data Streaming using an Online Bin-packing Strategy

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The stream processing framework HarmonicIO is a prototype that addresses the needs for processing streams based on relatively large individual objects. In this regard, it is a specialized streaming framework well-suited for scientific workflows. Salman Toor and Oliver Stein presented this work, and our latest publication  Smart Resource Management for Data Streaming using an Online…

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Applied Cloud Computing, Data-Intensive Computing, HASTE, News, publication, Software

Sadi Alawadi joins the lab as a postdoc focusing on FedML

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It is great to be able to welcome Sadi Alawadi to the lab! Sadi will work in our project scalable federated machine learning. This postdoc project is part of an eSSENCE initiative is connected to the new cross-disciplinary effort AI4Research at Uppsala University, with the aim to specifically support this effort by research on new e-Science methods…

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FedML, News

Addi Ait-Mlouk joins the lab as postdoc focusing on FedML

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It is great to be able to welcome Addi-Ait Mlouk to the lab! Addi will work in our project scalable federated machine learning. This postdoc project is part of an eSSENCE initiative is connected to the new cross-disciplinary effort AI4Research at Uppsala University, with the aim to specifically support this effort by research on new e-Science methods…

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FedML, News

MSc thesis opportunities in privacy-preserving Machine Learning

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We have opportunities for a number of MSc thesis students to work with the group in the spring semester 2020. 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. In this project we work…

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Applied Cloud Computing, FedML, News, Open Positions

Challenges

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The practical problems that arise when going from simple models to big models include: Most simulation algorithms do not scale well to high dimensions. Simulation can becomes prohibitively expensive due to the multiscale nature of systems. Failure of traditional engineering methodology such as sensitivity analysis and optimization due to high dimensionality, non-linearities and stochasticity…. ……

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Anass and Stefan moves on to new adventures

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It is with mixed feelings that we say goodbye to Stefan and Annas. Both have contributed with lots of energy and science to the group, but will now move on. Stefan to a role in industry, and Anass to an assistant professorship at Ecole Centrale Casablanca. You will both be missed, and thank you so…

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News, Uncategorized

Highly Scalable Federated Machine Learning

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Artificial intelligence is rapidly transforming our society. Machine learning models will soon be in every digital system we use. For this reason, there is an urgent need for methods and software that allows for development of state-of-the art ML models while protecting the integrity of data owners . In this project we work on algorithms,…

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Machine learning-assisted analysis of stochastic biochemical reaction networks

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Biochemical reaction networks represent complex cellular regulatory mechanisms. These networks are typically analyzed using discrete stochastic simulation models. The models typically involve numerous reactions involving a large number of chemical species, governed by highly uncertain parameters. Likelihood-free parameter inference Given existing data pertaining to a biochemical reaction network, one is often interested in inferring the…

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