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 / RA
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Diganta Misra

PhD Student

The purpose of our research is to design new optimizers and neural networks to accelerate technology and scientific discovery with Deep Learning. 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).

Teaching at the University of Tübingen: Nonconvex Optimization for Deep Learning (Winter Semester 24/25), Details here.