Yiming Ying, Professor
School
of Mathematics and Statistics
Tel: +61 493906954
Email: mathying [at] gmail.com
yiming.ying@sydney.edu.au
Research interests: My research interests are mainly in the areas of
Statistical Learning Theory, Machine Learning, Optimisation, Trustworthy AI,
and Mathematics of Data Science. I
am also very keen to apply Machine Learning methods to solve real-data analysis
problems. The goal of my research is to address the computational and
statistical challenges arising in modern data analysis, helping the discovery
of hidden information in big and complex data.
News and Open Positions:
Postdoc
and fully funded PhD positions are available in my group. Please contact me yiming.ying@sydney.edu.au for any
inquiries.
Short Bio: Dr. Ying got his PhD in
Mathematics from Zhejiang University in 2002 and completed his postdoc training
in machine learning at CityU in Hong Kong, and at UCL
and the University of Bristol in UK.
Before joining the University of Sydney on 12/2023, he was a 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. He was also a member of the UA Machine Learning Group. Prior to that, he was a Lecturer (Assistant Professor)
in the Department of Computer
Science at the University of Exeter, UK from 2010 to 2014.
Dr. Ying is the 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. His research projects have been funded by NSF, IBM, EPSRC, Simons Foundation, and NHS Foundation Trust. He currently serves
as an associate editor of Transactions on Machine Learning Research,
Neurocomputing, and Analysis and Applications, and Mathematical Foundation of
Computing. He regularly serves as a (Senior) Program Member/Area Chair for
major machine learning conferences such as NeurIPS,
ICML, and AISTATS, and a panel member for various research councils such as
NSF, EPSRC, and RGC of Hong Kong.
Research Grants:
· PI, FRAPP: Fair,
Robust, And Privacy-Preserving Machine Learning Algorithms, SUNY-IBM AI Research Alliance, 2021-2022.
· PI, Robust Deep Learning with
Big Imbalanced Data, National
Science Foundation (NSF), IIS-2110546,
2021-2024. (Collaborative project with University of Iowa)
· PI, New Studies of Learning with
Stochastic Convex Optimization, National
Science Foundation (NSF), DMS-2110836, 2021-2024.
· Co-PI, A Study of New Aggregate
Losses for Machine Learning, National Science
Foundation (NSF), IIS-2008532 , 2020-2023.
· PI, Online AUC Maximization Algorithms
for Streaming Data, National Science Foundation (NSF), IIS-1816227 , 2018- 2021.
· PI, Theory and Algorithms for
Nonstandard Prediction Problems, Simons Foundation (Collaboration Grants for
Mathematicians), No. 4422504, 2016-2021. (Terminated at 2018 due to the award
of NSF)
· PI, Advanced peer to peer transactive energy platform with
predictive optimization, Department of Energy (subaward from Ecolong), 2018-2019.
· PI, Metric Learning for Big data, Presidential Innovation Fund for Research and Scholarship (PIFRS) from
UAlbany, 2016- 2017.
· PI, Towards a New Generation of
Matrix Learning Methods in Machine Learning, Engineering and Physical Sciences Research Council (EPSRC), EP/J001384/1 (UK), 2/2012-6/2013.
· PI, Towards Automatic Prediction of Tumor Growth from CT Images, Royal Devon and Exeter NHS Foundation Trust (UK), 2012- 2013.
Publications: see my Google Scholar Profile