Prof YU, Leung Ho Philip   楊良河
Head / Professor
Department of Mathematics and Information Technology
Phone No: (852) 2948 7819
Email: plhyu@eduhk.hk
Contact
ORCiD
0000-0002-9449-0420
Phone
(852) 2948 7819
Email
plhyu@eduhk.hk
Scopus ID
7403599794
ResearcherID
D-3154-2009
Research Interest

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 Interest

Courses Taught in 2020-21:

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

External Appointment

Honorary Professor, Department of Computer Science, The University of Hong Kong, since 9/2020.

Professional Profile

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.

Research Interest

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 Interest

Courses Taught in 2020-21:

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

External Appointment

Honorary Professor, Department of Computer Science, The University of Hong Kong, since 9/2020.

Selected Output

Journal Publications
You, J., Yu, P.L.H., Tsang, A.C.O., Tsui, E.L.H., Woo, P.P.S., Lui, C.S.M., Leung G.K.K., Mahboobani, N., Chu, C.-Y., Chong, W.-H., Poon, W.-L. (2021). 3D Dissimilar-Siamese-U-Net for Hyperdense Middle Cerebral Artery Sign Segmentation. Computerized Medical Imaging and Graphics, 90 (June 2021), 101898.
K.K.F. LAW, W.K. LI and Philip L.H. YU (2021). An Alternative Nonparametric Tail Risk Measure. Quantitative Finance, 21(4), 685-696.
Philip L. H. YU and Wai Keung LI (2021). Project-based Learning via Competition for Data Science Students. Harvard Data Science Review, 3(1), 1-4.
Chiu, W. H. K., Vardhanabhuti, V., Poplavskiy, D., Yu, P. L. H., Du, R., Yap, A. Y. H., Zhang, S., Fong, A. H. T., Chin, T. W. Y., Lee, J. C. Y., Leung, S. T., Lo, C. S. Y., Lui, M. M. S., Fang, B. X. H., Ng, M. Y. and Kuo, M. D. (2020). Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs. Journal of Thoracic Imaging, 35(6), 369-376.
Seto, W. K. W., Chiu, W. H. K., Yu, P. L. H., Cao, W., Cheng, H. M., Wong, E. M. F., Wu, J., Lui, G. C. S., Shen, X., Mak, L. Y., Li, W. K. and Yuen, R. M. F. (2020). An end-to-end artificial intelligence model accurately diagnosing hepatocellular carcinoma on computed tomography. United European Gastroenterology Journal, 8(8 suppl), 48-49.
Seto, W., Chiu, K., Yu, P. L. H., Cao, W., Cheng, H. M., Lui, G., Wong, E. M. F., Wu, J., Mak, L. Y., Shen, X. P., Li, W. K. and Yuen, M. F. (2020). High diagnostic performance of a deep learning artificial intelligence model in accurately diagnosing hepatocellular carcinoma on computed tomography. Hepatology, 72 (1 Suppl), 84-85.
Yu, P. L. H., Ng, F. C., and Ting, J. K. W. (2020). Adjusting covariance matrix for risk management. Quantitative Finance, 20(10), 1681-1699.
K.K.F. LAW, W.K. LI and Philip L.H. YU (2020). Evaluation Methods for Portfolio Management. Applied Stochastic Models in Business and Industry, 36(5), 857-876.
Lu, R., Yu, P. L. H., and Wang, X. (2020). Sparse vector error correction models with application to cointegration‐based trading. Australian & New Zealand Journal of Statistics, 62(3), 297-321.
K. LAW, W.K. LI and P. YU (2020). An Empirical Evaluation of Large Dynamic Covariance Models in Portfolio Value-at-Risk Estimation. Journal of Risk Model Validation, 14(2), 21-39.
Lu, R., and Yu, P. L. H. (2020). Smooth buffered autoregressive time series models. Journal of Statistical Planning and Inference, 206, 196-210.
You, J., Tsang, A. C. O., Yu, P. L. H., Tsui, E. L. H., Woo, P. P. S., Lui, C. S. M., and Leung, G. K. K. (2020). Automated hierarchy evaluation system of large vessel occlusion in acute ischemia stroke. Frontiers in Neuroinformatics, 14, 14.
Zhu, Y., Yu, P. L. H., and Mathew, T. (2020). Improved estimation of optimal portfolio with an application to the US stock market. Journal of Statistical Theory and Practice, 14(1), 25.
Tsang, A. C. O., You, J., Li, L. F., Tsang, F. C. P., Woo, P. P. S., Tsui, E. L. H., Yu, P. L. H. and Leung, G. K. K. (2020). Burden of large vessel occlusion stroke and the service gap of thrombectomy: A population-based study using a territory-wide public hospital system registry. International Journal of Stroke, 15(1), 69-74.

Project

Research and Development of Artificial Intelligence in Educational and Financial Technologies
..
Project Start Year: 2021, Principal Investigator(s): YU, Leung Ho Philip 楊良河
 
Cross-lingual Image Captioning
..
Project Start Year: 2020, Principal Investigator(s): YU, Leung Ho Philip 楊良河
 
Modeling Ranking Data in Social Networks
Ranking of items arises in many situations in our daily lives. Very often, not all the items are ranked, resulting in a set of incomplete ranking data. A typical example of incomplete ranking data is movie recommendation where users in a social media platform rated a number of movies and some of these users may be friends of each other. As not all movies are rated by the same user, after converting ratings to rankings, such dataset becomes a set of incomplete rankings with friendship connections . . .
Project Start Year: 2020, Principal Investigator(s): YU, Leung Ho Philip 楊良河