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Published: 23/03/2023 on IT Warsaw Days


Michał Affek, Embedded Machine Learning Researcher and Jan Glinko, Machine Learning Researcher at are among the speakers at the IT Warsaw Days conference.

IT Warsaw Days is a rich agenda of several hundred speeches and the largest parallel IT and data science job fair for students and professionals in Poland. At the event you will find topics covering all major areas of IT and data science, such as: Trends in technology, Testing, Data Science / Big Data, Cloud & Ops, Hardware / IoT,
Programming, IT Security, Project Management, Gamedev / VR & AR, UX / UI, IT careers.

Our speakers

Michał Affek is an Embedded Machine Learning Researcher at He is currently enrolled in an industrial PhD programme at the Gdansk University of Technology. His main interests are remote sensing (processing done specifically on satellites), machine learning algorithms for edge devices, and parallel computing.

Topic: Satellite on-board data processing  

The topic revolves around data processing on remote a edge device. Namely, challenges related to such low-resource computing with the use of NVIDIA’s Jetson platform will be presented. A brief introduction to machine learning algorithms, followed by an in depth explanation of performance optimization methods for their use with limited resources in a harsh environment.

Portrait of Michał Affek, Machine Learning Researcher at

Jan Glinko works as a Machine Learning Researcher at He graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. He is interested in applying synthetic datasets for learning deep neural networks and in learning algorithms to reduce the amount of data required for effective network training.

Topic: Meta-learning for fast Neutral Network fine-tuning

The speed of learning, whether it is pattern recognition or the acquisition of new skills, is a characteristic of human intelligence. Meta-learning is a field of machine learning that attempts to replicate how humans learn new tasks. This type of fast and flexible learning, benefiting from previous experience, differs from the standard approach to training deep neural networks. In summary, our goal is no longer a model that generalizes well, but becomes one that adapts well.

Key information about IT Warsaw Days

30.03 (online) | 1.04 (onsite)

Faculty of Mathematics and Information Sciences
Warsaw University of Technology
Koszykowa 75, Warsaw

Register here.

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