Projects per year
Personal profile
Personal profile
Philip Yu is a Professor and Head of the Departement 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 was 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.
Research interests
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 and AI Education.
Teaching Interests
Courses Taught in 2021-22:
MTH6184 Data Mining and STEM Education (MA(MP) & MSc(AI&EdTech) course, 2nd semester)
Professional information
External Appointments
Honorary Professor, Department of Computer Science, The University of Hong Kong, since 9/2020.
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Projects
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Research and Development of Artificial Intelligence in Educational and Financial Technologies
30/06/21 → 30/06/24
Project: Research project
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淺談總體比例的置信區間估算法
楊良河 & 陳昊, 2022, 《學校數學通訊》. 香港: 香港特別行政區政府教育局課程發展處數學教育組, Vol. 25. p. 106-115Research output: Chapter in Book/Report/Conference proceeding › Chapters
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3D Dissimilar-Siamese-U-Net for hyperdense middle cerebral artery sign segmentation
YOU, J., YU, L. H. P., TSANG, A. C. O., TSUI, E. L. H., WOO, P. P. S., LUI, C. S. M., LEUNG, G. K. K., MAHBOOBANI, N., CHU, C., CHONG, W. & POON, W., Jun 2021, In: Computerized Medical Imaging and Graphics. 90, 101898.Research output: Contribution to journal › Articles › peer-review
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An alternative nonparametric tail risk measure
LAW, K. K. F., LI, W. K. & YU, L. H. P., 2021, In: Quantitative Finance. 21, 4, p. 685-696Research output: Contribution to journal › Articles › peer-review
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Effectiveness of filter trading as an intraday trading rule
XIN, L., LAM, K. & YU, L. H. P., 27 Jul 2021, In: Studies in Economics and Finance. 38, 3, p. 659-674Research output: Contribution to journal › Articles › peer-review
3 Citations (Scopus) -
Project-based learning via competition for data science students
YU, L. H. P. & LI, W. K., 2021, In: Harvard Data Science Review. 3, 1Research output: Contribution to journal › Articles › peer-review
Open Access