Xu Yiwei

From July 2023 to December 2024, I worked as a Research Assistant in Professor Yang Kai's group at Soochow University, focusing on the interdisciplinary fusion of AI and nanobiomaterials physics. This role allowed me to explore innovative approaches to harnessing machine learning for modeling and analyzing nanomaterial interactions, leading to novel insights in biomaterial design. Collaborating with a dynamic and forward-thinking team, I contributed to projects that bridged cutting-edge AI techniques with experimental and theoretical research, igniting a passion for solving complex scientific challenges through multidisciplinary collaboration.

In this project, I applied machine learning techniques to simultaneously enhance the antimicrobial efficacy and reduce the toxicity of antimicrobial peptides. This research deepened my understanding of how machine learning can be used to optimize molecular design, offering a promising approach to develop safer and more effective therapeutic peptides.

In this project, we explored how machine learning models could be used to reverse engineer the self-assembly process of nanostructures. By combining AI technology with self-assembly theory from physical chemistry, this groundbreaking study aimed to understand the driving forces and regulatory mechanisms of self-assembly at the microscopic level, opening new possibilities for nanomaterial design.

This project focused on developing a medical diagnostic system for systemic lupus erythematosus (SLE) based on graph neural networks. The system aims to improve diagnostic efficiency and accuracy by using multimodal data inputs and graph neural network-based multimodal analysis. It provides doctors with precise diagnostic insights, facilitating better disease identification and treatment strategies.