Scholar
Yiming Ying
Google Scholar ID: xnA_lMMAAAAJ
School of Mathematics and Statistics, University of Sydney
Citations & Impact
Citations
3,203
H-index
28
i10-index
66
Publications
20
Co-authors
59
list available
Publications
Resume (English only)
Academic Achievements
- Recipient of the University of Exeter Merit Award in 2012, University at Albany’s Presidential Award for Excellence in Research and Creative Activities in 2022, and SUNY Chancellor’s Award for Excellence in Scholarship and Creative Activities in 2023. Research projects funded by ARC, NSF, IBM, EPSRC, Simons Foundation, and NHS Foundation Trust. Serves as an associate editor of Transactions on Machine Learning Research, Neurocomputing, Analysis and Applications, and Mathematical Foundation of Computing. Regularly serves as a (Senior) Program Member/Area Chair for major machine learning conferences such as NeurIPS, ICML, and AISTATS, and a panel member/reviewer for various research councils such as NSF, EPSRC, and RGC of Hong Kong.
Research Experience
- Lecturer (Assistant Professor) in the Department of Computer Science at the University of Exeter, UK from 2010 to 2014. Tenured Professor in the Department of Mathematics and Statistics, College of Arts and Sciences, and affiliated with the Department of Computer Science at SUNY Albany. Founding director and a member of the UALBANY Machine Learning Group. Currently at the University of Sydney.
Education
- PhD in Mathematics from Zhejiang University in 2002, completed postdoc training in Machine Learning at CityU in Hong Kong, and at UCL and the University of Bristol in UK.
Background
- Research interests: Statistical Learning Theory, Machine Learning, Optimization for Machine Learning, Trustworthy AI, and Mathematics of Data Science. Also keen to apply Machine Learning methods to solve real-data analysis problems, and interested in cancer informatics such as AI tools for early detection of cancers.
Miscellany
- Current research projects include Stochastic Optimization for Machine Learning, Deep Learning Theory, Differential Privacy, Fair Machine Learning, and Robust Deep Learning for Big Imbalanced Data.
