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--- aira:start [2026/05/21 15:24] – [2026-05-21] mzk
+++ aira:start [2026/05/28 14:30] (current) – [Schedule Spring 2026] mtm
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 ===== Schedule Spring 2026 =====
+  * **[PHD TRACK] 2026.05.28**: Soheila Molaei,  senior researcher @ University of Oxford, [[#section20260528|From Graphs to Graph Neural Networks: Foundations and Applications in Healthcare]]
+    * Meeting link:[[https://teams.microsoft.com/meet/377493387003987?p=TVBcvsqGs42CdT7U9T|MS Teams]]
+    * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQC8c4XxDZvTQ70h5GCoIrRwAYBNQyagBm3DaEdsBovDMNQ?e=8fwmHj|View]]
+    * Presentation slides:  TDA
   * **[RESEARCH TRACK] 2026.05.21**: Jan Argasiński,  assistant professor @ Jagiellonian University, [[#section20260521|Computational Neuroscience at Sano (Centre for Computational Medicine): Current Research and a Spotlight on “A Tract Density Biomarker for Survival Prediction in Glioblastoma”]]
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+==== 2026-05-28 ====
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+{{ :aira:soheila_molaei_foto.png?200| }}
+</WRAP>
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+**Speaker**: Soheila Molaei,  Senior researcher @ University of Oxford
+**Title**: From Graphs to Graph Neural Networks: Foundations and Applications in Healthcare
+**Abstract**:
+In this talk, I will introduce the foundations of graph theory and graph neural networks, starting from intuitive examples of real-world graphs such as social networks, molecules, road networks, and biomedical interaction networks. I will explain why graph-structured data challenges standard machine learning assumptions, and how GNNs use message passing and representation learning to model complex relationships. The talk will then cover common GNN tasks, including node classification, link prediction, and graph classification, before introducing key models such as graph convolutional networks and graph attention networks. I will conclude with applications in healthcare, including medical graphs, drug discovery, and patient modelling.
+**Biogram**:
+Soheila Molaei is a senior researcher in the Department of Engineering Science at the University of Oxford. Her research focuses on artificial intelligence and machine learning, particularly graph neural networks, federated learning, neuro-symbolic AI, and learning from multimodal and heterogeneous data, with applications in complex real-world and healthcare domains.
+</WRAP>
+<WRAP clear></WRAP>
 ==== 2026-05-21 ====