Regular biography
Yiming Ying is a Professor of Statistical Data Science in the School of Mathematics and Statistics at The University of Sydney. Based in the Carslaw Building, Ying's research focuses on statistical data science, though specific research areas are not detailed in available sources. Ying is affiliated with the university's mathematics department and is listed in the School's people directory. Further details about Ying's academic background, publications, and professional achievements are not provided in the current profile information.
Scholar-generated biography
Yiming Ying is a researcher in the School of Mathematics and Statistics at the University of Sydney, focusing on Statistical Learning Theory, Optimization for ML, Trustworthy AI, and LLM. His work explores theoretical foundations of machine learning, including support vector machines, distance metric learning, and stochastic optimization. He has contributed to areas such as AUC maximization, multi-task learning, and generalization bounds for metric and similarity learning. His research emphasizes the development of robust and interpretable AI systems, particularly in the context of trustworthy AI and large-scale learning. His publications often address challenges in online learning, regularization, and the analysis of learning algorithms.