معرفی
Saeideh Ghanbari Azar is a doctoral student at the Department of Computing Science, Umeå University. She is based at MIT House, Umeå University (MIT.E.255), with research centered on machine learning challenges, particularly the generalization gap. Her contact email is saeideh.ghanbari@umu.se.
Research Focus: Her work addresses theoretical-practical discrepancies in machine learning, including:
- Generalization gap analysis
- Machine learning pitfalls
- Model robustness
- Algorithm evaluation
Publications: She co-authored a key study titled From promise to practice: a study of common pitfalls behind the generalization gap in machine learning in the Transactions on Machine Learning Research. This work highlights trends in algorithm design, overfitting, and practical implementation challenges.
Collaborations: She is a group member of the Machine Learning research group at Umeå University, contributing to interdisciplinary research in computational methods.
