Read more about our upcoming events below.
VILMA seminar

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 ( for more details.


VILMA seminar schedule

02.02.2023 at 14:15 CoE: Virtual laboratory for molecular level atmospheric transformations (VILMA) - Sub-seminar 1.

  • Siddharth Iyer, Tampere University: "Molecular rearrangement of bicyclic peroxy radicals: key route to aerosol from aromatics."
  • Vitus Besel, University of Helsinki: "Curation of Big Data for Atmospheric Science."

16.03.2023 at 14:15 CoE: Virtual laboratory for molecular level atmospheric transformations (VILMA) - Sub-seminar 2. 

  • 14.15-15.15 Otso Ovaskainen, University of Jyväskylä: "Surveying the world’s biodiversity with DNA, audio, image, machine learning, statistics and mathematics." (Via Zoom)
  • 15.15-15.30 Coffee break
  • 15.30-16.00 Timo Pekkanen, University of Helsinki: "The Chemical Master Equation". (On-site)

04.05.2023 at 14:15 CoE: Virtual laboratory for molecular level atmospheric transformations (VILMA) - Sub-seminar 3.

  • 14.15-15.15 Jari Kaipio, University of Eastern Finland: "What makes a problem an unstable (inverse) one and how to interpret it?" (On-site)
  • 15.15-15.30 Coffee break
  • 15.30-16.00 Aku Seppänen, University of Eastern Finland: "Tomographic imaging of greenhouse gases using open-path laser dispersion spectroscopy." (Tentatively via Zoom)

01.06.2023 at 14:15 CoE: Virtual laboratory for molecular level atmospheric transformations (VILMA) - Sub-seminar 4. 

  • 14.15-15.15 Juho Rousu, Aalto University: "Machine Learning for small molecule identification from mass spectrometry data."
  • 15.15-15.30 Coffee break
  • 15.30-16.00 Ivo Neefjes, University of Helsinki: "Configurational sampling for ion mobility modeling."


Autumn 2023

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

Machine Learning for Earth Observation (MACLEAN) workshop

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: 

  • Supervised and unsupervised machine learning methods 
  • Semi-supervised classification, domain adaptation, active learning, structured output learning, multi-task learning, and online learning
  • Interpretability and explainability of machine learning methods
  • Bayesian modelling of various parts of EO or atmospheric processes
  • Dimensionality reduction and feature selection, finding embeddings and latent variables
  • Visualisation and interaction with EO and atmospheric data
  • Interactive model building and eliciting expert knowledge
  • Applications of high-performance computing 


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 for more information and updates. See the ECML PKDD 2023 website at 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