International Workshop on Machine Learning for Space Weather: Fundamentals, Tools and Future Prospects | (smr 3750)
Starts 7 Nov 2022
Ends 11 Nov 2022
Buenos Aires - Argentina
This is a hybrid meeting
This workshop aims to foster Space Weather research through the application of Machine Learning (ML) and statistical techniques by providing the participants with theoretical and practical training on Space Weather and Machine Learning fundamentals, with hands-on tutorials.
The complex and highly coupled Sun-Earth system is constantly being monitored by ground and space-based instrumentation which produces a huge amount of daily data. These datasets, in addition to the increasing computing capability, are regularly used to produce forecasting models and other Space Weather products. In particular, Space Weather data analysis and modeling using ML techniques are showing promising results.
The purpose of the workshop is to give theoretical and tailored practical training on Machine Learning fundamentals, its application to Space Weather and future prospects, covering also important topics like Research to Operations (R2O), explainable Artificial Intelligence (XAI) and trustworthiness and ethics.
A limited number of grants are available to support the attendance of selected participants, with priority given to participants from Latin America and other developing countries. There is no registration fee.
Female candidates are encouraged to apply.
Applicants can submit a ‘Research Abstract’. A small number of abstracts may be selected for a contributed talk.
Deadline for Applications:
4 September, 2022 - for participation in presence.
- Space Weather fundamentals
- Space Weather Gaps and applications that can be tackled with Machine Learning
- Machine Learning Basic Concepts and Tools
- Deep Learning and current trends
- Machine Learning techniques applied to Space Weather and their main challenges
- Discussion on R2O, XAI, trustworthiness and ethics on ML
- Open source tools for ML (Python, scikit-learn, Keras, etc).
Sharafat Gadimova (UNOOSA - ICG), Keith Groves (Boston College), Yenca Migoya Orué (ICTP), María Graciela Molina (FACET-UNT / CONICET), ICTP Scientific Contact: Bruno Nava (ICTP)