51±¾É«

My research program focuses on developing new statistical tools to analyze, model, and interpret complex systems such as human dynamics and high dimensional multimodal data arising from medical research. This research lies at the intersection of modern statistical learning and "traditional" statistical ideas. Currently I am focusing on two research directions:

• Develop efficient statistical models and algorithms for describing, analyzing and interpreting human behaviour data, such as email communication, emergency calls etc. The ultimate goal of the first research direction is to propose a flexible model, which does not require domain knowledge and is interpretable.
• Develop automated statistical learning tools for analyzing high dimensional multimodal data. To overcome the difficulties in analyzing high dimensional multimodal data from multiple sources, we propose to develop a hybrid approach that interactively combines methods from statistics, computational topology and deep learning.

• Merit Award in Teaching and Service, 51±¾É« (2023)
• SOSCIP-Mitacs Accelerate Grant (2022)
• Donald F. Morgenson Awards for Teaching Excellence in the Category of Innovation, 51±¾É« (2021)
• NSERC COVID-19 Alliance Award (2020 - 2021)
• Faculty of Science Teaching for Excellence Award, 51±¾É« (2020)
• Merit Award in Teaching and Research, 51±¾É« (2019)
• Mitacs Accelerate Grant (2019)
• NSERC Individual Discovery Grant (2019-2025)
• NSERC Engage Grant (2018)
• Merit Award in Teaching, 51±¾É« (2018)
• National Research Council-Industrial Research Assistance Program (2016)
• Outstanding Teaching Award, St. Francis Xavier University (2016)
• NSERC Individual Discovery Grant (2010-2017)
• NSERC early researcher award (2011-2017)

I have research assistantship opportunities for undergraduate and graduate students interested in developing statistical learning and data mining techniques.

I am willing to supervise graduate students in the areas of statistical learning, data mining and deep learning.

Proceeding Editor

• Addressing Modern Challenges in the Mathematical, Statistical, and Computational Sciences - Proceedings of the VI AMMCS International Conference, Waterloo, Canada, August 14-18, 2023. Co-editors: D. Marc Kilgour, Herb Kunze, Roman Makarov, Roderick Melnik, Xu Wang. 2024, Springer.
• Recent Developments in Mathematical, Statistical and Computational Sciences - Proceedings of the V AMMCS International Conference, Waterloo, Canada, August 18-23, 2019. Co-editors: D. Marc Kilgour, Herb Kunze, Roman Makarov, Roderick Melnik, Xu Wang. August, 2021, Springer.
• Advances in Data Science – Proceedings of the Second Women in Data Science and Mathematics (WiSDM) Workshop from July 29 to August 2, 2019 at the Institute for Computational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, US, Brown University, Providence, RI, US. Co-editors: Ilke Demirr, Yifei Lou, Xu Wang, Kathrin Welker. December, 2021, Springer.
• Recent Advances in Mathematical and Statistical Methods for Scientific and Engineering Applications - Proceedings of the IV AMMCS International Conference, Waterloo, Canada, August 20-25, 2017. Co-editors: D. Marc Kilgour, Herb Kunze, Roman Makarov, Roderick Melnik, Xu Wang. 2018, Springer.

Publications 

• Wang, S.*; Wang, X.; Li, C.* (2023) Modeling Terror Attacks with Self-Exciting Point Processes and Forecasting the Number of Terror Events. The Special Issue “Recent Advances in Statistical Theory and Applications”, invited publication by guest editors Xiaoping Shi and Augustine Wong, Entropy 2023, 25, 1011.
• Xiang, C.H.*; Zhao, Y.; Wang, X.; Ye, P.X. (2023) Approximation by the Extended Neural Network Operators of Kantorovich Type. Mathematics 2023, 11, 1903.
• Longlong Feng*, Xu Wang (2021). Automate Obstructive Sleep Apnea Diagnosis Using Convolutional Neural Networks. AMMCS 2019: Recent Developments in Mathematical, Statistical and Computational Sciences. PROMS, Volume 342, Pages 521-531. Online, 30 August 2021.
• Emily T. Winn*, Marilyn Vazquez, Prachi Loliencar, Kaisa Taipale, Xu Wang, and Giseon Heo (2021). A Survey of Statistical Learning Techniques as Applied to Inexpensive Pediatric Obstructive Sleep Apnea Data. Advances in Data Science, Volume 26, Pages 291-328.
• Chenlong Li*, Zhanjie Song, Xu Wang. (2019) Mining Periodic Cascading Bursts Pattern in Individual Email Communication. Journal of Applied Statistics. 46 (14), 2603-2626.
• Chenlong Li, Zhanjie Song, Xu Wang. (2019) Modelling and Estimation of Clustering Phenomena in Emergency Calls Using Spatial-temporal Self-exciting Point Processes. IEEE Access. Issue Date: December 2019. Volume: 7, Issue: 1. Pages 24865-24876.
• Giseon Heo, Kathryn Leonard, Xu Wang, and Yi Zhou*. (2019) High-dimensional Multiple Scaled Data Analysis of Obstructive Sleep Apnea Study with Interdisciplinary Endeavor. Research in Data Science, Springer. Page 81-107.
• Bigelow, A. E., Power, M., MacLean, K., Gillis, D., Ward, M., Taylor, C., Berrigan, L., & Wang. X. (2018). Mother-infant skin-to-skin contact and mother-child interaction nine years later. Social Development, 27, 937-951.

Note: The names marked by * are students.

• ST494/ST694: Statistical Learning
• ST462/ST662: Advanced Regression Analysis
• ST362/ST562: Regression Analysis
• ST260: Introduction to Statistics
• ST231: Statistical Methods for Life and Health Sciences
• DATA200: Data Analytics
• DATA100: Introduction to Data Science

• 51±¾É« welcomes high school students from across Canada to prestigious STEAM program
• 51±¾É« to welcome Grade 10 and 11 students from across Canada for annual Shad program
• 51±¾É« recognizes teaching excellence with Donald F. Morgenson awards
• 51±¾É« to welcome Grade 10 and 11 students from across Canada virtually as part of inaugural Shad 51±¾É« program
• COVID-19 case competition research in the Business, Technology and Innovation section of 51±¾É« Research Annual Report
• 51±¾É« student researchers use data analysis to build COVID-19 fake news detector; Using mathematical modelling to help the planet in the industry section of 51±¾É« Research Annual Report
• 51±¾É« researchers, Lovell Corporation collaborate on app to help meet UN Sustainable Development Goals