Dennis Adamek
Image:
Petter Bjørklund / SFI Visual Intelligence

Dennis Adamek

Meet Dennis, our newest Doctoral Research Fellow

We happily welcome Dennis Adamek, who recently started as a Doctoral Research Fellow at Visual Intelligence in Tromsø in April 2025.

Meet Dennis, our newest Doctoral Research Fellow

We happily welcome Dennis Adamek, who recently started as a Doctoral Research Fellow at Visual Intelligence in Tromsø in April 2025.

By Petter Bjørklund, Communications Advisor at SFI Visual Intelligence

Adamek has a B.Sc and an M.Sc in Physics from Friedrich Schiller University (FSU) in Germany. At FSU, he specialized in optics, photonics and spectroscopic data analysis. His master's thesis focused on interpretable hyperspectral image analysis for medical applications.

Before starting his PhD project, he worked as a Research and Hyperspectral Application Specialist at Norsk Elektro Optikk AS (NEO), a privately owned research company within the field of electro optics. NEO is an industry-leading manufacturer of airborne and ground-based hyperspectral imaging systems.

Adamek's PhD project will focus on fast uncertainty estimation in deep learning-based object detection for subsea monitoring applications. For many applications, decisions need to be made in real time based on some model output, which requires an estimate of uncertainty to understand whether the output is trustworthy or not.

"Most established methods for uncertainty estimation in deep learning are computationally expensive and time-intensive—which limits their usability in those cases. We will investigate sparse Bayesian methods and try optimizing other established techniques in terms of speed while still producing accurate and reliable uncertainty estimates", Adamek explains.

He will further investigate how synthetic data can be used to understand and improve uncertainty estimation in deep neural networks. To start with, Adamek will apply the developed methods on sonar acoustic and video sequence data for tasks like automatic seabed gas flare or underwater nature type detection and explore their applicability to other subsea applications.

"I am excited to contribute to the development of fast uncertainty estimation methods in deep learning for object detection, which can help make subsea monitoring more robust and trustworthy.  I look forward to working in a highly interdisciplinary and international environment, gaining new insights while advancing the real-world deployment of uncertainty-aware AI systems", Adamek says.

Adamek's UiT employee page

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