From May 27 to June 1, 2024, the Advanced School on Applied Machine Learning seminar, hosted by the International Centre for Theoretical Physics (UNESCO-ICTP), was held in Trieste, Italy. Dr. Li Weikai, from Mathematics and Statistics School of Chongqing Jiaotong university, was invited to attend the event and showcased the latest research achievements in the field of machine learning from our university.

During the conference, over 50 renowned experts and scholars from around the world delivered keynote presentations on various aspects of applied machine learning, including high-performance computing, physical information neural networks, graph convolutional networks, and spiking neural networks. On behalf of our university, Dr. Li Weikai presented research findings titled “Rapid Online Small-Sample Object Detection Based on Prototype Learning.” This study utilizes innovative methods to enhance the efficiency and accuracy of small-sample object detection, providing a new perspective and solution for the field of machine learning. The proposed approach received recognition and praise from the participating experts.

Dr. Li Weikai engaged in in-depth discussions with Professor Caroline from the University of Hamburg, Germany, and reached a preliminary cooperation agreement on a machine learning task for cosmic nebula detection based on small sample learning. This cooperation programme is expected to start within this year, opening up new paths for our research in related fields.

The active participation and sharing of the School in the international symposium has secured valuable academic resources for our researchers and further enhanced our academic influence in the field of machine learning. The participants fully recognised the research results of our university and expressed their expectation for more academic exchanges and cooperation opportunities in the future. We also hope to establish wider cooperation with global research institutions in future research and innovation, and jointly promote the progress of science and technology.