Chinese Name : 楊良河

Philip Yu is a Professor at the Department of Mathematics and Information Technology of the Education University of Hong Kong.  He was the Chairperson of the Asian Region Section of the International Association of Statistical Computing, the Vice President of the Hong Kong Statistical Society, and a member of the Technical Committee of Computational Finance and Economics, IEEE Computational Intelligence Society.  He is also an Associate Editor of Frontiers in Artificial Intelligence, Digital Finance, and Computational Statistics.  Professor Yu obtained his Bachelor of Science degree in Mathematics (First class honor) and a PhD degree in Statistics from the University of Hong Kong.

His research interests are broad; they include AI and big data analytics, non-parametric inference, ranking methods, time series analysis, financial data analysis, risk management and statistical trading.  He has a substantial volume of work on most of these topics, including two co-authored books on nonparametric statistics and more than 120 publications in conference proceedings and professional journals such as Biometrika, Journal of Royal Statistical Society Series A, Biometrics, Journal of Business and Economic Statistics, Journal of Statistical Software, Statistics and Computing, Expert Systems with Applications, and IEEE Transactions on Neural Networks and Learning Systems.

Professor Yu has been continuously engaged in performing outstanding teaching and mentoring activities, providing exceptional service to the statistics profession through numerous conferences and committee work, and promoting statistical literacy in Hong Kong through a number of outreach activities. He has been involved in the organizing and program committees in many international conferences. He is a member of Assessment Working Group of the Chief Executive’s Award for Teaching Excellence (2020/2021).  He has many years of rich experience in various contract research/consulting projects for business, industry and public bodies including banks and insurance company, stock exchanges, hospital authority, etc.

• Data Mining and Machine Learning. AI and Big Data Analytics. Text Analytics.
• Preference Learning. Analysis of Discrete Choice and Ranking Data.
• Statistical Methods in Finance. FinTech. Statistical Trading. Quantitative Risk Management.
• Environmental Statistics. Ranked Set Sampling.
• Statistical Ranking and Selection.

• Courses Taught in 2020-21:

  • MTH4155 Applied Probability (UG course, 1st semester)
  • MTH6184 Data Mining and STEM Education (MAMP course, 2nd semester)

ORCID : 0000-0002-9449-0420

Scopus ID: 7403599794

Researcher ID: K-6763-2013