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Model exploration using active learning

Posted on October 27, 2016October 16, 2020 By admin No Comments on 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 we are interested in the scenario where a modeler has no or very limited prior knowledge about what type of qualitative interesting behavior the model can display over the large parameter space. The tools we develop should help the modeler discover those behaviors with a small computational budget, and as little manual work as possible. By utilizing human-in-the-loop machine learning  we are developing a smart parameter sweep workflow. An example is shown in the image below, where a high-dimensional parameter sweep application is augmented with automated feature extraction and clustering, followed by training a model for classification based on user-defined labels (such as interesting or non-interesting realizations). With this model, the smart sweep application will learn to more efficiently explore areas of interestingness in the parameter space.  

Recent publications

F. Wrede and  A. Hellander (2018), Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning, BioRxiv (submitted).

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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
A_HellanderAndreas Hellander@A_Hellander·
11 May

Are you using StochSS? Please help us gather insights into what is working well and what can be improved by filling in this short survey https://forms.gle/mEqfASuUd3MDWuPS9

@LindaPetzold @briandrawert @mhucka

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A_HellanderAndreas Hellander@A_Hellander·
9 May

Apply to this PhD student position in the eSSENCE and SciLifeLab graduate school in data-intensive science!

This project is the intersection of cybersecurity and big data with main supervisor @sztoor.
https://www.uu.se/en/about-uu/join-us/details/?positionId=501061

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A_HellanderAndreas Hellander@A_Hellander·
28 Apr

PhD position in the eSSENCE/@scilifelab graduate school in data-intensive science with @cnettel: https://uu.se/en/about-uu/join-us/details/?positionId=501716 Apply and be part of a new interdiciplinary research effort @UU_University!

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A_HellanderAndreas Hellander@A_Hellander·
23 Apr

If you are a current user of StochSS please let us know your thoughts by filling out this brief user survey: https://forms.gle/3r836iph8gqFEpZX7

#systemsbiology #stochss @LindaPetzold @briandrawert @prashant_rsingh

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AssistSweASSIST Sweden@AssistSwe·
14 Apr

Soon the partners in the ASSIST project will attend a workshop on federated learning arranged by Scaleout. Partners from different countries (Sweden, Belgium, Netherlands, Turkey) will contribute with nodes that train a segmentation network.

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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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