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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Alexandru Meterez

Research Intern
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Radenko Tanasic

Research Intern

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).

If you're a young scientist (e.g. bachelor or master student) facing societal, financial, or personal challenges, we would love to contribute to your development by offering you a fully-funded 3-month project! Please apply to the CaCTüS Internship.