Speaker at the ECMWF-ESA workshop


More information here

The use of Machine Learning/Deep Learning (ML/DL) techniques is becoming widespread in a large and ever-growing number of application areas in Earth System Observation and Prediction (ESOP). Additionally, the scale, complexity and sophistication of the ML/DL technologies applied in ESOP has also increased considerably over the last few years, reflecting the growing uptake of ML/DL ideas in the ESOP communities and benefiting from increased interest of ML/DL domain scientists and of large commercial players. As a result, ML/DL tools are increasingly integrated in ESOP applications and in some areas they show promise of substituting traditional methodologies.

The third edition of the ECMWF–ESA Workshop on Machine Learning for Earth Observation and Prediction aims to provide an up-to-date snapshot of the state of the art in this rapidly evolving field and to facilitate discussion among scientists and practitioners about the current opportunities and challenges in the use of ML/DL technologies for ESOP.
Thematic areas

Thematic areas that we expect to be covered in this workshop include:

  1. Machine Learning for Earth Observations
  2. Hybrid Machine Learning - Data Assimilation
  3. Machine Learning for Model emulation and Model discovery
  4. Machine Learning for user-oriented Earth Science applications
  5. Machine learning at the edge and high-performance computing Attendance

The workshop will be held at ECMWF Shinfield Park (Reading, UK) from 14 to 17 November 2022.

Paper presented by me during the workshop:

  • A. Sebastianelli, D. Spiller, R. Carmo, J. Wheeler, A. Nowakowski, Jacobson, D. Kim, H. Barlevi, Z. El Raiss Cordero, F. J Colón-González, R. Lowe, S. L. Ullo, and R. Schneider, “A reproducible ensemble machine learning approach to forecast dengue outbreaks,” To be submitted to IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2022.