In the spring of 2023, VILMA seminar is organized as a sub-seminar of INAR seminar in Physicum E204, Kumpula Campus, with the possibility of attending remotely via Zoom. The intended audience is members of VILMA and INAR but we also welcome external people who are interested in our work. If you are interested in joining the seminar, please contact VILMA coordinator Laura Kippola (firstname.lastname@example.org) for more details.
02.02.2023 at 14:15 CoE: Virtual laboratory for molecular level atmospheric transformations (VILMA) - Sub-seminar 1.
16.03.2023 at 14:15 CoE: Virtual laboratory for molecular level atmospheric transformations (VILMA) - Sub-seminar 2.
04.05.2023 at 14:15 CoE: Virtual laboratory for molecular level atmospheric transformations (VILMA) - Sub-seminar 3.
01.06.2023 at 14:15 CoE: Virtual laboratory for molecular level atmospheric transformations (VILMA) - Sub-seminar 4.
21.09.2023 at 14:15 CoE: Virtual laboratory for molecular level atmospheric transformations (VILMA) - Sub-seminar. Thomas Berkemeier, Max Planck Institute for Chemistry: "Machine Learning methods for molecular property estimation, surrogate modelling, and directing laboratory experiments."
05.10.2023 at 14:15 CoE: Virtual laboratory for molecular level atmospheric transformations (VILMA) - Sub-seminar. Claudia Mohr, Stockholm University: - TBA
Turin, Italy, 18 September 2023
The workshop is part of ECML PKDD 2023
12 June 2023: Paper submission.
12 July 2023: Notification of acceptance.
The vast amount of data currently produced by modern Earth Observation (EO) missions and measurements on the surface has raised new challenges for the Remote Sensing Community and atmospheric modellers. EO sensors can now offer (very) high spatial resolution images with revisit time frequencies never achieved before considering different signals, e.g., multi(hyper)spectral optical, radar, LiDAR, and Digital Surface Models.
On the other hand, atmospheric composition and processes are measured on the surface, starting from molecular scale measurements with mass spectrometers, particle counters, and more traditional meteorological instruments. Modern machine learning techniques can be crucial in dealing with such heterogeneous, multi-scale, and multi-modal data.
Some methods gaining attention in this domain include deep learning, domain adaptation, semi-supervised approach, time series analysis, active learning, explainable artificial intelligence, uncertainty quantification, and interactive model building and visualisation. Even though machine learning and the development of ad-hoc techniques are gaining popularity, we still see a significant need for more interaction between domain experts and machine learning researchers.
This workshop aims to be an international forum where machine learning researchers and domain experts can meet each other to exchange, debate, and draw short and long-term research objectives around the exploitation and analysis of EO and atmospheric data via Machine Learning techniques. Among the workshop’s goals, we want to give an overview of the current machine-learning research dealing with EO and other atmospheric measurement data. On the other hand, we want to stimulate concrete discussions to pave the way to new machine learning frameworks especially tailored to deal with such data.
The non-exclusive list of topics for the workshop includes, to the extent related to the EO and atmospheric processes:
We welcome original contributions, either theoretical or empirical, describing ongoing
projects or completed work. Contributions can be of two types: short position papers (up to 6 pages, including references) or full research papers (up to 10 pages, including references). Papers must be written in LNCS format, i.e., accordingly to the ECML-PKDD 2023 submission format. Accepted contributions will be made available electronically through the Workshop web page. Springer will publish the post-proceedings.
See the workshop website at https://sites.google.com/view/maclean23/ for more information and updates. See the ECML PKDD 2023 website at https://2023.ecmpkdd.org/ for more details about the venue.
Thomas Corpetti, CNRS, LETG-Rennes COSTEL UMR 6554 CNRS, Rennes, France
Dino Ienco, INRAE, UMR Tetis, Montpellier, France
Roberto Interdonato, CIRAD, UMR Tetis, Montpellier, France
Minh-Tan Pham, Univ. Bretagne-Sud, UMR 6074, IRISA, Vannes, France
Patrick Rinke, Aalto University, Helsinki
Kai Puolamäki, University of Helsinki, Helsinki, Finland