I am a Machine Learning engineer. I recently finished my PhD studies at École Polytechnique Fédérale de Lausanne in the INDY Lab, advised by Prof. Patrick Thiran. I am interested in the generalization ability of deep neural networks with a particular focus on classification tasks with limited and/or noisy-labeled datasets. In my research, I focus on finding techniques that require no access to ground truth labels while being informative about the model generalization and the dataset itself.

Curriculum Vitae


  • July 2023: I successfully defended my PhD thesis titled “Deep Learning Generalization with Limited and Noisy Labels”. You can find it here.



Leveraging Unlabeled Data to Track Memorization

Mahsa Forouzesh, Hanie Sedghi, Patrick Thiran

ICLR 2023, ArXiv, OpenReview, Github

Disparity Between Batches as a Signal for Early Stopping

Mahsa Forouzesh, Patrick Thiran

ECML/PKDD 2021, ArXiv, Published Version, Github

Generalization Comparison of Deep Neural Networks via Output Sensitivity

Mahsa Forouzesh, Farnood Salehi, Patrick Thiran

ICPR 2020, Oral Presentation, ArXiv, Published Version, Github


Differences Between Hard and Noisy-labeled Samples: An Empirical Study

Mahsa Forouzesh, Patrick Thiran

ArXiv, Github