IRIS-HEP Fellow: Maxym Naumchyk
Fellowship dates: Jul – Aug, 2022
Jun – Sep, 2023
Jul – Sep, 2024
Home Institution: Igor Sikorsky Kyiv Polytechnic Institute
Project: Integrating ML algorithms for LHC data compression into the ESCAPE Virtual Research Environment
Integrate one of the current state-of-the-art ML algorithms designed for LHC data compression into the ESCAPE Virtual Research Environment as a part of the European Open Science Cloud. The successful results of the project will open the possibility of extending the use of this algorithm to other experiments and fields.More information: My project proposal
Mentors:
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Caterina Doglioni (University of Manchester)
Project: Machine Learning on Network Data for Problem Identification
To provide a more effective method of identifying certain types of network issues using machine learning so that such problems can be quickly resolved before they impact scientists who rely on these networks.More information: My project proposal
Mentors:
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Shawn McKee (University of Michigan)
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Petya Vasileva (University of Michigan)
Project: Adding new features to the Awkward-Array library
To add new QOL features to the Awkward library and to improve interconnection with similar features for ragged arrays, like RaggedTensor in TensorFlow's library and NestedTensor in PyTorch.More information: My project proposal
Mentors:
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Ianna Osborne (Princeton University)
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Jim Pivarski (Princeton University)
- 14 Sep 2022 - "Integrating ML algorithms for LHC data compression into the ESCAPE Virtual Research Environment", Maxym Naumchyk, IRIS-HEP Fellowship Presentations Recording: Integrating ML algorithms for LHC data compression into the ESCAPE Virtual Research Environment
- 16 Oct 2023 - "Machine Learning on Network Data for Problem Identification", Maxym Naumchyk, Recording: Machine Learning on Network Data for Problem Identification
Current Status
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