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| 1 | +--- |
| 2 | +layout: post |
| 3 | +title: "Welcome to Our Research Blog" |
| 4 | +date: 2025-10-03 10:00:00 +0100 |
| 5 | +author: NGMLGroup |
| 6 | +excerpt: "Welcome to the Northernmost Graph Machine Learning Group's research blog! Here we share insights, breakthroughs, and stories behind our cutting-edge research in graph neural networks and machine learning." |
| 7 | +--- |
| 8 | + |
| 9 | +Welcome to the **Northernmost Graph Machine Learning Group**'s research blog! |
| 10 | + |
| 11 | +Based at the [UiT the Arctic University of Norway](https://en.uit.no/) in Tromsø, our group activity is dedicated to basic research in machine learning. |
| 12 | +We also apply our research to a variety of domains, including energy analytics and climate science. |
| 13 | + |
| 14 | +We're excited to launch this blog as a platform to share our journey, insights, and breakthroughs with the broader community. |
| 15 | +Alongside the formal presentation in our papers, we aim to complement it here with a format that’s more relaxed, visual, and hands-on. In addition, we plan to publish tutorials that guide readers through key methods and concepts that we use in our work, making it more approachable to both researchers and practitioners. |
| 16 | + |
| 17 | +## Our Research Focus |
| 18 | + |
| 19 | +Our work spans several exciting areas: |
| 20 | + |
| 21 | +- 🎱 **Graph Pooling**: Techniques for down-sampling graph structures while preserving important information ([library](https://torch-geometric-pool.readthedocs.io/en/latest/), [AB25](https://arxiv.org/pdf/2409.05100?), [CB25](https://arxiv.org/abs/2501.09821), [GZB+22](https://arxiv.org/pdf/2110.05292)); |
| 22 | +- 📊 **Spatiotemporal modeling**: Using graphs to model complex temporal dependencies ([HCB25](https://openreview.net/forum?id=MHQXfiXsr3), [MAB24](https://arxiv.org/pdf/2402.10634), [CMB+23](https://ojs.aaai.org/index.php/AAAI/article/view/25880)); |
| 23 | +- 🎯 **Uncertainty Quantification**: Assessing and mitigating uncertainty in forecasting in both structured and unstructured data domains ([NCB+25](https://arxiv.org/abs/2510.05060), [CJM+25](https://arxiv.org/pdf/2502.09443), [GSB23](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10360823)); |
| 24 | +- 🔬 **Interpretability**: Making spatiotemporal models more transparent and trustworthy ([GSB24](https://arxiv.org/pdf/2410.13469), [GSS+23](https://arxiv.org/pdf/2209.07926)); |
| 25 | +- ⚡ **Scalability**: Breaking barriers to handle massive real-world datasets ([NCB+25](https://arxiv.org/abs/2510.05060), [CMB+23](https://ojs.aaai.org/index.php/AAAI/article/view/25880)). |
| 26 | + |
| 27 | +## What You'll Find Here |
| 28 | + |
| 29 | +In this blog, we'll be sharing: |
| 30 | + |
| 31 | +- **Research insights** from our latest publications; |
| 32 | +- **Educational content** to make complex concepts accessible; |
| 33 | +- **Practical applications** of graph neural networks; |
| 34 | +- **Updates** on our group activities, collaborations and events. |
| 35 | +- **Behind the scenes** looks at our research process and team dynamics. |
| 36 | + |
| 37 | +--- |
| 38 | + |
| 39 | +*Stay tuned for our upcoming posts, where we'll dive deep into our latest research.* |
| 40 | + |
| 41 | +**The NGMLGroup Team** |
| 42 | +*Northernmost Graph Machine Learning Group* |
| 43 | +*UiT the Arctic University of Norway* |
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