CEDAR email: [Session 7] Data Science Applied to Ionospheric Studies : BSS 2025 – Beacon Satellite Symposium

[Maria Graciela Molina]

Dear colleagues,

Please consider sending your contributed talk to session 7 " Data Science
(Advanced Statistical and Machine Learning Techniques) Applied to
Ionospheric Studies" of the upcoming BSS2025 -Beacon Satellite Symposium to
be held in Rome from 10-14 November 2025.

Some relevant info:
Deadline for abstracts submission: June 15th, 2025.
More details:  https://bss2025.ingv.it/

About the session:
*7. Data Science (Advanced Statistical and Machine Learning Techniques)
Applied to Ionospheric Studies.*

*Chairs: Jade Morton (Univ. of Colorado, US), Claudio Cesaroni (INGV),
Maria Graciela Molina (FACET-UNT, Argentina)*

The ionosphere’s impact on radio propagation is well-established, but
accurately modeling these phenomena remains a challenge due to their
complexity, many unknown aspects, and the incomplete understanding of
ionospheric processes. Over the past few decades, advanced statistical and
machine learning techniques have found widespread application across
scientific fields, offering powerful tools to address complex physical
scenarios that require more flexible and sophisticated modeling. Key
advancements include uncovering correlations between diverse data sets and
enabling computationally efficient predictions.

The application of these techniques to geosciences has evolved from a
“proof of concept” phase to one where real-world research and operational
applications are now possible. This session aims to showcase the current
and next phase of applying advanced statistical and machine learning
methods to ionospheric studies. Presentations will focus on using these
techniques for ionospheric characterization,nowcasting and forecasting, and
understanding their effects on radio propagation.

We invite contributions that explore the full spectrum of data science
applied to the ionosphere, from data collection and management to analysis
and communication. Topics of interest include, but are not limited to,
efficient data management, correlation analysis between various ionospheric
phenomena, prediction and forecasting of critical ionospheric variables
using data-driven models, establishing causal relationships between
ionospheric data and other phenomena, and comparing observed versus
model-generated ionospheric data. We particularly encourage innovative
ideas on how data science and machine learning can reshape the future of
ionospheric research.

Best regards,
Jade, Claudio and Graciela


-------------------------------------------
*Dra. María Graciela Molina*
Prof. Asociada FACET-UNT / Associate Professor FACET -UNT
Inv. Adjunta CONCET / Researcher CONICET
Investigadora Asociada INGV/ Associated researcher INGV

Av. Independencia 1800, Tucumán - Argentina
Tel: +54-381-4364093 (ext.7765)
gmolina at herrera.unt.edu.ar /
*m.graciela.molina at gmail.com* <m.graciela.molina at gmail.com>
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