Research Group
Deep Models and Optimization

Investigating the interplay between optimizer and architecture in Deep Learning, and new networks for long-range reasoning.

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Niccolò Ajroldi

  • Research Engineer
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Destiny Okpekpe

Master Thesis Student
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Nursena Köprücü

Research Intern
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Felix Sarnthein

PhD Student
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Si Yi Meng

PhD Research Intern
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Vera Milovanovic

Master Thesis Student / Assistant

The purpose of our research is to design new optimizers and neural networks to accelerate scientific discovery through efficient and reliable training. Our approach is theoretical, with a strong focus on optimization theory as a tool for understanding the challenging dynamics of modern neural networks.
We strongly believe deep learning will revolutionize science and technology, offering solutions to society's most pressing challenges. With a stronger theoretical foundation, we envision a future where scientists and engineers, regardless of their resource limitations, can leverage powerful and reliable deep learning solutions to help make the world a better place.

If you like our mission, please apply for CLS, ELLIS, IMPRS-IS PhD Programs (deadline Nov 15th).

We are organizing the Next Generation of Sequence Modeling Architectures Workshop at ICML 2024; please submit your insights on attention, SSMs, and RNNs! Deadline May31st AoE.