Ammar Kheder Ammar Kheder, portrait illustrated by Mitsuhiro Arita

I build forecasts of the air we breathe, at the scale where people live. My models compute what physics knows, how the atmosphere carries pollution from where it was emitted, and learn the rest with generative networks. The latest, Foehn, forecasts PM2.5 at 1 km over all inhabited land up to a week ahead, as calibrated ensembles (preprint, October 2026). Based at LUT University in Lahti, with the Atmospheric Modelling Centre (AMC-Lahti) and INAR, University of Helsinki.

I have hands-on experience running large-scale distributed training on the LUMI supercomputer, scaling experiments up to 1,024 AMD MI250X GPUs for training vision transformer models on high-resolution atmospheric reanalysis data.

MSc in Engineering (Big Data & AI) from EiCnam Paris, with a one-year apprenticeship at INRIA Bordeaux within the Mnemosyne team, co-led by Frédéric Alexandre and Nicolas Rougier.

Keywords. Air quality, atmospheric modelling, generative models (flow matching), Lagrangian transport, physics-informed neural networks, vision transformers, high-performance computing, large-scale distributed training.

Download CV (PDF) ↓

1 km

Foehn, global forecasts
7 days ahead, all inhabited land

npj

Nature Portfolio
1 publication, 2026

10

Citations
across 4 works

1,024

GPUs scaled
LUMI / AMD MI250X

Research

Three questions, one line of work: where does the air come from, what does it carry, and how do we forecast it at the scale where people live?

News

  1. September 2026 Presented TopoFlow, our physics-guided Vision Transformer for high-resolution air quality prediction, at IAC 2026 (International Aerosol Conference) in Xi’an, China, with great conversations with the community on day one.
  2. August 2026 Invited talk at the Boya Forum on Environmental Research, Peking University College of Environmental Sciences and Engineering, Beijing, presenting “Encoding Topography, Wind, and Cross-Scale Atmospheric Context into Vision Transformers for High-Resolution Air Pollution Forecasting” and sharing recent work on TopoFlow and CRAN-PM, hosted by Dongjie Shang.
  3. June 2026 Research visit to the ARCHES team at Inria, hosted by Claire Monteleoni, to strengthen the France–Finland collaboration.
  4. Apr 2026 TopoFlow published in npj Climate and Atmospheric Science (Nature Portfolio).
  5. May 2026 Two-day research stay (5–6 May) with AMC member Prof. Jia Chen and her group at the Technical University of Munich (TUM), marking the start of a new international collaboration.
  6. Apr 2026 Talk accepted at IAC 2026 (International Aerosol Conference), Xi’an, China.

Publications

  1. TopoFlow: topography-aware pollutant Flow learning for high-resolution air quality prediction

    A. Kheder, H. Toropainen, W. Peng, S. Antão, J. Chen, M. Boy, Z.-S. Liu

    npj Climate and Atmospheric Science, Nature Portfolio, 2026.

    BibTeX
    @article{kheder2026topoflow,
      title   = {TopoFlow: topography-aware pollutant flow learning for high-resolution air quality prediction},
      author  = {Kheder, Ammar and Toropainen, Henri and Peng, Wei and Ant{\~a}o, Samuel and Chen, Jia and Boy, Michael and Liu, Zhi-Song},
      journal = {npj Climate and Atmospheric Science},
      year    = {2026},
      doi     = {10.1038/s41612-026-01417-5}
    }
  2. CRAN-PM: Cross-Resolution Attention Network for High-Resolution PM2.5 Prediction

    A. Kheder et al.

    Preprint, 2026.

    BibTeX
    @misc{kheder2026cranpm,
      title         = {CRAN-PM: Cross-Resolution Attention Network for High-Resolution PM2.5 Prediction},
      author        = {Kheder, Ammar and others},
      year          = {2026},
      eprint        = {2603.11725},
      archivePrefix = {arXiv},
      url           = {https://arxiv.org/abs/2603.11725}
    }
  3. Inverse Neural Operator for ODE Parameter Optimization

    Z.-S. Liu, W. Peng, H. Toropainen, A. Kheder, A. Rupp, H. Froning, X. Lin, M. Boy

    Preprint, 2026.

  4. Deep Spatio-Temporal Neural Network for Air Quality Reanalysis

    A. Kheder, B. Foreback, L. Wang, Z.-S. Liu, M. Boy

    Scandinavian Conference on Image Analysis (SCIA), Springer LNCS, 2025.

    BibTeX
    @inproceedings{kheder2025aqnet,
      title     = {Deep Spatio-Temporal Neural Network for Air Quality Reanalysis},
      author    = {Kheder, Ammar and Foreback, Benjamin and Wang, Lili and Liu, Zhi-Song and Boy, Michael},
      booktitle = {Scandinavian Conference on Image Analysis (SCIA)},
      series    = {Lecture Notes in Computer Science},
      publisher = {Springer},
      year      = {2025},
      doi       = {10.1007/978-3-031-95911-0_6}
    }

Experience

2024 – nowJunior Researcher & Teaching Assistant →
LUT University, Lahti, Finland
2023 – 2026CEO, Wabel Group
AI services and web development
2021 – 2022Research Engineer (Apprenticeship)
INRIA Bordeaux, Mnemosyne team
2021 – 2023Volunteer Firefighter →
Sapeurs-pompiers des Deux-Sèvres

Education

2024 – nowPh.D. Computational Engineering
LUT University, Finland
2021 – 2024MSc Engineering, Big Data & AI
EiCnam Paris, with apprenticeship at INRIA Bordeaux
2019 – 2021Bachelor, Data Science
BUT Niort. Statistics, Big Data, Business Intelligence
2016 – 2017BIA Aeronautical Initiation →
Lycée Paul-Guérin, Niort