Machine Learning in Heliophysics
Welcome to the 2022 ML-Helio Conference!
This year we are offering the conference in a hybrid format. All of the technical sessions will be live on Zoom as interactive meetings, not webinars. Virtual participants will be seen and heard in the meeting space and will be able to interact in real-time with the in-person attendees.
Our conference management platform is Whova . This will provide all attendees with an easy-to-navigate schedule of events, with embedded Zoom links and access to the technical session recordings at the end of each day. Whova offers value to both virtual and in-person attendees and is available as an app or through a browser. We will send more information about Whova and how to use it as we get closer to the conference.
Your paid registration will grant you access to both Whova and our conference-specific Slack channel.
If you have any questions or comments, please email us at email@example.com.
We're looking forward to seeing you, in-person or virtually!
The goal of the ML- Helio conference is to leverage the advancements happening in disciplines such as machine learning, deep learning, statistical analysis, system identification, and information theory, in order to address long-standing questions and enable a higher scientific return on the wealth of available heliospheric data.
We aim at bringing together a cross-disciplinary research community: physicists in solar, heliospheric, magnetospheric, and aeronomy fields as well as computer and data scientists. ML- Helio will focus on the development of data science techniques needed to tackle fundamental problems in space weather forecasting, inverse estimation of physical parameters, automatic event identification, feature detection and tracking, times series analysis of dynamical systems, combination of physics-based models with machine learning techniques, surrogate models and uncertainty quantification.
The conference will consists of classic-style lectures, complemented by hands-on tutorials on Python tools and data resources available to the heliophysics machine learning community.
The conference will be hosted in hybrid mode (in-person and virtual).
We expect all the participants of Machine Learning in Heliophysics to follow our Code of Conduct.
COVID update: The conference organizers request that only fully vaccinated people (as defined by CDC guidelines ) or otherwise holding a valid medical exemption attend the conference in person. Attendees are expected to follow all CDC, state of Colorado, and CU Boulder public health orders, guidelines and policies in place at the time of the conference.
More info: https://ml-helio.github.io