workshops
Upcoming Workshops & Sessions
🚀 Connecting AI & Carbon Science
The AI4Carbon Initiative fosters cutting-edge discussions on machine learning applications in carbon cycle research. Our workshops bring together leading researchers, industry experts, and emerging talents to tackle critical challenges in atmospheric transport modeling, carbon monitoring, and verification.🌍 EGU 2026 – Vienna, Austria
Main Session: Machine Learning & Carbon Cycle Science
We are hosting a comprehensive session on the interplay of carbon cycle science, monitoring, and carbon markets with machine learning at EGU 2026. Researchers warn that current observation-based constraints on the global carbon cycle entail large uncertainties. Meanwhile, policy-driven needs for carbon sink quantification are increasingly addressed by innovative industry solutions. This session brings together diverse communities for rigorous discussion on measuring and verifying carbon cycles.
Keynote Speaker: Christian Igel, University of Copenhagen – A distinguished ML expert who has led major initiatives on mapping global forest tree height and biomass from remote sensing data.
🔗 Full Details: Machine learning and hybrid modelling for carbon cycle science, monitoring and carbon market policy
View Session
Splinter Meeting: AI4Carbon Inverse Modeling
An exclusive by-invitation-only splinter meeting to explore potential future work on AI for atmospheric transport and inverse modeling.
📅 Date & Time: Tuesday, 5 May, 16:15–18:00 CEST
🏢 Location: Room 2.43
👥 Format: Short presentations + discussion/round table
📋 Organizers: Vitus Benson | Co-organizer: Elena Fillola
Short presentations covering atmospheric transport models, inverse modeling challenges, and the role of AI/ML in advancing this field.
📊 EGU 2025 – Vienna, Austria
Greenhouse Gas Inversions & Machine Learning
We co-hosted a session on greenhouse gas inversions using both conventional and machine learning approaches. The session featured high-quality presentations and posters, including a keynote talk by Abhishek Chatterjee (NASA JPL) on OCO-2 and OCO-3 progress in satellite-based carbon monitoring.
🔗 Session: Understanding feedbacks between greenhouse gas exchange processes and climate variability
💻 1st Virtual Workshop
Machine Learning Meets Atmospheric Transport
Our inaugural virtual AI4Carbon workshop brought together researchers and practitioners for in-depth discussions on applying machine learning to atmospheric transport modeling and carbon cycle research.
⏱️ Duration: ~2 hours
🕐 Global Times: 5pm CET / 4pm GMT / 11am EST / 8am PST / 11pm ICT
Workshop Format
Hour 1: Expert Presentations
Four 15-minute presentations covering atmospheric transport models, inversion techniques, and current research challenges.
Featured Speakers
Hour 2: Interactive Discussion
We discussed current challenges in atmospheric tracer transport modeling and inversion, and explored how recent advances in AI/ML can facilitate carbon cycle research, including:
- Transport model development and optimization
- Data assimilation techniques
- Uncertainty quantification
- Integration of neural network methods
Discussion Topics
Pre-Workshop Survey
We invited all participants to share perspectives on transport models and inversion methods through a comprehensive survey. Your insights help guide our research priorities and identify critical areas for collaborative development.
📋 View the AI4Carbon Survey</div>