Yiyang Li is a PhD student at the Hong Kong Polytechnic University, focusing on stochastic multiobjective optimisation theories and algorithms, with applications in machine learning and artificial intelligence. She earned both her master’s degree in Statistics (with distinction) and bachelor’s degree in Mathematics (with first-class honors) from Imperial College London. Yiyang has been involved in a range of projects, including graph neural network (GNN) predictions with dynamic embeddings and simulations, convolutional neural network (CNN) predictions for spatiotemporal data, deep learning models combined with stochastic Brownian motions for financial product price prediction, and training recurrent neural networks (RNNs) for sequential data using a novel stochastic algorithm. Her work not only delivers practical solutions but also provides theoretical convergence guarantees, advancing multiobjective optimisation and its applications in AI.