This poster presented a machine learning framework for classifying gravitational wave signals using neural networks. Focusing on time-frequency representations of LIGO data, the study demonstrated how convolutional neural networks (CNNs) can distinguish between true astrophysical signals and noise artifacts. The approach aimed to enhance the sensitivity and reliability of gravitational wave detection pipelines by reducing false positives and improving event classification accuracy. The poster was awarded 3rd place for excellence in research presentation, highlighting the impact and innovation of the work within the field of gravitational wave astrophysics.

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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Daniel Fredin

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