PhD Student Attends GAMM 2024 Conference in Magdeburg, Germany

There were plenary lectures, mini-symposia, and parallel sections covering various disciplines in applied mathematics and mechanics. With over 1,000 participants, the event provided a platform for sharing research and encouraging collaboration in mathematical modeling, simulation, and optimization.
 
Ahmet Pala, Phd student at CRIMAC, presented his research work about using self-supervised learning to analyze acoustic data in fisheries. He explained how we can use this method to understand the sounds fish make underwater, which is important for managing fisheries. By using a self-supervised technique called DINO, he and his co-workers, trained the model to learn from acoustic data without needing manual annotations, which helps save time and resources. Their findings show that this approach can identify different patterns in the acoustic data, which can be useful for studying marine environments. This research is important because it fills a gap in using advanced techniques for analyzing fisheries data.