Research Interests and Overview

My general research areas are data mining, business analytics, and statistical computing. I am interested in developing methodology, models, and algorithms for discovering useful knowledge from data, which can support real-world decision making. Specifically, my research topics may be clustered into a methodological arm (computing and machine learning) and an applied arm (analytics of business, social, and behavioral data).

The following is a list of select publications. A full publication list may be found in my curriculum vita (PDF).

Select Journal Papers

  1. Du, B., Zhou, W., Liu, C., Cui, Y., and Xiong, H. (2019). Transit pattern detection using tensor factorization. INFORMS Journal on Computing (JOC), Volume 31, Issue 2, pp. 193-206.
  2. Du, B., Liu, C., Zhou, W., Hou, Z., and Xiong, H. (2019). Detecting pickpocket suspects from large-scale transit records. IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume 31, Issue 3, pp. 465-478.
  3. Zhou, W. , Xiong, H., Duan, L., Xiao, K., and Mee, R. (2018). Paradoxical correlation pattern mining. IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume 30, Issue 8, pp. 1561-1574.
  4. Guo, Y., Zhou, W., Luo, C., Liu, C., and Xiong, H. (2016). Instance-based credit risk assessment for investment decisions in P2P lending. European Journal of Operational Research (EJOR). Volume 249, Issue 2, pp. 417-426.
  5. Zhou, W. and Xiong, H. (2011). Checkpoint evolution for volatile correlation computing. Machine Learning. Volume 83, Issue 1, pp. 103-131.
  6. Xiong, H., Zhou, W., Brodie, M., and Ma, S. (2008). Top-k φ correlation computation. INFORMS Journal on Computing (JOC). Volume 20, Issue 4, pp. 539-552.

Select Conference Papers

  1. Du, B., Tong, Y., Zhou, Z., Tao, Q., and Zhou, W. (2018). Demand-aware charger planning for electric vehicle sharing. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), pages 1330–1338. London, United Kingdom.
  2. Ye, Z., Zhang, L., Xiao, K., Zhou, W., Ge, Y., and Deng, Y. (2018). Multi-user mobile sequential recommendation: An efficient parallel computing paradigm. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), pages 2624–2633. London, United Kingdom.
  3. Wang, X., Reger, R. K., Zhou, W., and Williams, D. W. (2017). From home-country shared grievance to cross-border social disapproval: social media coverage, national animosity, and nationalism as integrated mobilizations. In: The 2017 Academy of Management (AOM) Annual Meeting, Atlanta, Georgia. Best Student Paper Award.
  4. Zhou, W., Zhu, Y., Javed, F., Rahman, M., Balaji, J., and McNair, M. (2016). Quantifying skill relevance to job titles. In: Proceedings of the 2016 IEEE International Conference on Big Data (BigData 2016), pages 1532–1541, Washington D.C.
  5. Zhou, W., Jin, H., and Liu, Y. (2012). Community discovery and profiling with social messages. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012), pp. 388-396.

Book Chapters

  1. Edirisinghe, C. and Zhou, W. (2014). Portfolio optimization using rank correlation. Chapter 167 in: J. Wang (Ed), Encyclopedia of Business Analytics and Optimization (5 Volumes) (ISBN: 978-146665202-6), pp. 1866-1879. Hershey, PA: IGI Global.
  2. Xiong, H., Steinbach, M., Tan, P.-N., Kumar, V., and Zhou, W. (2009). Pattern preserving clustering. In: John Wang (Ed), Encyclopedia of Data Warehousing and Mining, Second Edition (ISBN: 978-1-60566-010-3), Volume III, pp. 1505-1510. Hershey NY: Idea Group Inc., Information Science Reference.
  3. Xiong, H., Tan, P.-N., Kumar, V., and Zhou, W. (2007). Mining hyperclique patterns: a summary of results. In: F. Masseglia, P. Poncelet, and M. Teisseire (Eds), Data Mining Patterns: New Methods and Applications (ISBN: 978-159904162-9), pp. 57-84. Hershey PA: Idea Group Inc., Information Science Reference.

Patent

  1. Community Profiling for Social Media. U.S. Patent. With Jin, H. and Liu., Y.