Assistant Professor
Qingchen Wang received his Ph.D. in Data Science and Business Analytics from the University of Amsterdam. Qingchen’s research focuses on the development and application of machine learning and artificial intelligence techniques for solving real-life business problems. Selected research projects include data-driven driven consumer debt collection, pricing of parking reservations, workforce staffing and scheduling, and multi-channel conversion attribution for digital marketing.
Qingchen is also an expert practitioner of machine learning and predictive analytics, having consulted on a number of commercial data science projects, and has earned the title of “Grandmaster” on the international predictive analytics platform Kaggle.com.
Academic & Professional Qualification
Ph.D., University of Amsterdam
M.S. INSEAD
M.S. University College London
B.S. University of British Columbia
Teaching
Business Data Analysis (MBA)
Selected Publications
Working Papers
Data-driven Consumer Debt Collection via Machine Learning, with Ruben van de Geer and Sandjai Bhulai
Optimal Contact Center Staffing and Scheduling with Machine Learning, with Siqiao Li and Ger Koole
Multi-channel Conversion Attribution: A Machine Learning Approach, with Piet Pepperkorn and Maarten Soomer
Peer-reviewed Publications
Li, S., Wang, Q., and Koole, G., Predicting Call Center Performance with Machine Learning, Proceedings of the INFORMS International Conference on Service Science, 2018.
Puterman, M. L. and Wang, Q., Optimal Design of the PGA Tour; Relegation and Promotion in Golf, Proceedings of MIT Sloan Sports Analytics Conference, 2011.
Puterman, M. L. and Wang, Q., Optimal Dynamic Clustering Through Relegation and Promotion: How to Design a Competitive Sports League, Quantitative Analysis in Sports, 7, issue 2, Article 7, 2010.