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Highly Scalable Federated Machine Learning

Posted on March 23, 2019August 21, 2020 By admin No Comments on Highly Scalable Federated Machine Learning
Highly Scalable Federated Machine Learning

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

Posted on October 15, 2018October 16, 2020 By Prashant Singh No Comments on Machine learning-assisted analysis of stochastic biochemical reaction networks
Machine learning-assisted analysis of stochastic biochemical reaction networks

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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Hierarchical Analysis of Spatial and Temporal Data

Posted on May 30, 2017February 6, 2019 By admin No Comments on Hierarchical Analysis of Spatial and Temporal Data
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 to leverage the imminent explosion of image data from modern experimental setups in the biosciences. One central idea is to represent datasets as intelligently formed…

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Scalable simulation of stochastic multicellular systems

Posted on November 5, 2016February 6, 2019 By admin No Comments on Scalable simulation of stochastic multicellular systems
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, molecular processes in single cells, and models of multicellular systems comprising of large numbers of interacting cells such as bacterial colonies, tissue and tumors. In…

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StochSS: Stochastic Simulation Service

Posted on October 27, 2016February 4, 2019 By admin No Comments on StochSS: Stochastic Simulation Service
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), molecular interactions (stoichiometry, rate constants), and simulation goals (single trajectory, histogram, probabilities of rare events). The platform transparently executes model workflows using local resources (laptops,…

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Multiscale simulations of chemical kinetics

Posted on October 27, 2016October 3, 2017 By admin No Comments on Multiscale simulations of chemical kinetics
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 study—their function is dependent on a rich set of reaction networks. Important molecules inside a cell may exist in only a few copies, and that…

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Data-and simulation-driven life science. Much of our work in eScience and applied ML has applications in life science, and in Systems Biology in particular. We aim to enable data-and simulation-driven scientific discovery.

HASTE - a cloud native framework for intelligent processing of image streams: http://haste.research.it.uu.se/

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Andreas HellanderFollow

Andreas Hellander
Retweet on TwitterAndreas Hellander Retweeted
SciLifeLab_DCSciLifeLab_DataCentre@SciLifeLab_DC·
3 Nov

Join our great team at @SciLifeLab_DC!

We are now looking for IT-ansvarig SciLifeLab
👉Apply by Dec 12th.
👉More & apply here: https://www.kth.se/om/work-at-kth/lediga-jobb/what:job/jobID:546469/where:4/
👉More about @SciLifeLab_DC here: https://scilifelab.se/data

@scilifelab @KTHuniversity

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A_HellanderAndreas Hellander@A_Hellander·
25 Oct

Starting in 30mins :-)

Prashant Singh@prashant_rsingh

Join us tomorrow for an exciting seminar by @uPicchini on “guided sequential ABC schemes for intractable Bayesian models”. The seminar starts at 13.15 until 14.00 CEST in Room 101127, Ångströmlaboratoriet, Uppsala University & online: https://uu-se.zoom.us/j/65354024469. Warmly welcome!

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A_HellanderAndreas Hellander@A_Hellander·
6 Oct

eSSENCE, SERC and Chalmers e-science Centre are providing core e-science education to PhD students from the SeSE platform: https://sese.nu/

Researchers - get funding to develop and give a PhD course!
@uppsalauni @lunduniversity @umeauniversitet

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A_HellanderAndreas Hellander@A_Hellander·
6 Oct

Day two of the Swedish eScience Academy organized by eSSENCE.

Interesting to learn from Sverker Holmgren of Chalmers eCommons about the holistic approach to infrastructure and support for data centric research at Chalmers!

@UmeaUniversity @UU_University @lunduniversity

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A_HellanderAndreas Hellander@A_Hellander·
6 Oct

So great to be at the Swedish e-science Academy organized by #essenceofescience! Two days of scientific exchange between colleagues nationally, and in particular from the partner universities @UU_University @UmeaUniversity @lunduniversity.

Keynote day one by Kersti Hermansson.

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Decentralized AI, Federated Learning. One focus area of the group is development of methods and software to address decentralized and privacy-preserving AI. We are core contributors to the FEDn open source framework for scalable federated machine learning:

https://github.com/scaleoutsystems/fedn
Introduction to Federated Learning by Andreas Hellander
Join the discussion on Decentralized AI:

Scaleout Systems is a spin-out from ISCL on a mission to enable decentralized AI and federated learning to production.

https://www.scaleoutsystems.com/

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