blogpost init first draft

Author: RobertoNeglia

_posts/2025-10-03-welcome-to-our-blog.md

@@ -6,52 +6,39 @@ author: NGMLGroup
 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."
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-Welcome to the **Northernmost Graph Machine Learning Group's** research blog! 
+Welcome to the **Northernmost Graph Machine Learning Group**'s research blog!
 
-We're excited to share our journey of discovery in the fascinating world of graph neural networks, machine learning, and their applications to real-world problems. Based at the [UiT the Arctic University of Norway](https://en.uit.no/) in Tromsø, we're proud to be the northernmost research group working on these cutting-edge technologies.
+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. 
+We also apply our research to a variety of domains, including energy analytics and climate science.
 
-## What You'll Find Here
-
-In this blog, we'll be sharing:
-
-- **Research insights** from our latest publications
-- **Behind-the-scenes stories** of how our breakthroughs came to be
-- **Practical applications** of graph neural networks
-- **Educational content** to make complex concepts accessible
-- **Updates** on our group activities and collaborations
+We're excited to launch this blog as a platform to share our journey, insights, and breakthroughs with the broader community.
+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.
 
 ## Our Research Focus
 
 Our work spans several exciting areas:
 
-🧠 **Graph Neural Networks**: Developing new architectures and understanding their theoretical foundations
-
-📊 **Time Series Analysis**: Using graphs to model complex temporal dependencies
-
-🔬 **Interpretability**: Making AI systems more transparent and trustworthy
+TODO -> add links to papers where possible
 
-⚡ **Scalability**: Breaking barriers to handle massive real-world datasets
+- 🎱 **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));
+- 📊 **Spatiotemporal modeling**: Using graphs to model complex temporal dependencies ([HCB25](https://openreview.net/forum?id=MHQXfiXsr3), [MAB24](https://arxiv.org/pdf/2402.10634));
+- 🎯 **Uncertainty Quantification**: Assessing and mitigating uncertainty in forecasting in both structured and unstructured data domains ([CJM+25](https://arxiv.org/pdf/2502.09443), [GSB23](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10360823));
+- 🔬 **Interpretability**: Making spatiotemporal models more transparent and trustworthy ([GSB24](https://arxiv.org/pdf/2410.13469), [GSS+23](https://arxiv.org/pdf/2209.07926));
+- ⚡ **Scalability**: Breaking barriers to handle massive real-world datasets ([CMB+23](https://ojs.aaai.org/index.php/AAAI/article/view/25880)).
 
-🎯 **Applications**: From climate modeling to drug discovery, from social networks to financial markets
-
-## Why We're Writing This Blog
-
-Research papers are essential for advancing science, but they often miss the human story behind the discoveries. We want to share:
-
-- The **intuitions** that led to our breakthroughs
-- The **challenges** we faced along the way
-- The **real-world impact** of our work
-- The **mathematical beauty** behind the algorithms
+## What You'll Find Here
 
-## Join Our Journey
+In this blog, we'll be sharing:
 
-Whether you're a researcher, student, practitioner, or simply curious about the future of AI, we invite you to follow along as we explore the frontiers of graph machine learning.
+- **Research insights** from our latest publications;
+- **Educational content** to make complex concepts accessible;
+- **Practical applications** of graph neural networks;
+- **Updates** on our group activities, collaborations and events.
 
-The future is connected, and we're excited to discover what it holds together.
 
 ---
 
-*Stay tuned for our upcoming posts, where we'll dive deep into our latest research and share the stories behind the science.*
+*Stay tuned for our upcoming posts, where we'll dive deep into our latest research.*
 
 **The NGMLGroup Team**  
 *Northernmost Graph Machine Learning Group*