Organizers: Jason Bryant, Valyn Perini, and Andrew Vakhutinsky
The workshop is intended to serve as a forum for discussing analytics of hospitality revenue management and how it can be applied to the open problems facing hotel operators.
We plan a hybrid workshop consisting of several presentations followed by the panel discussion
Discrete Choice Models and Applications: Modeling, Estimation, and Optimization
Optimal Upselling of Hotel Rooms: A Discrete-Choice Approach
The Impact of Pricing on Cancellations in the Hotel Industry
Assortment and Price Optimization under MNL Model with Price Range Effect
Stefanus Jasin, Chengyi Lyu*, Huanan Zhang, Andrew Vakhutinsky
Panel Discussion: Hospitality Industry as an Opportunity for Developing New Analytical Solutions.
Valyn Perini, Jason Bryant, and Pelin Pekgün
Zifeng Zhao is an Assistant Professor of Business Analytics at the Mendoza College of Business, University of Notre Dame. He received B.S. in Financial Risk Management from the Chinese University of Hong Kong, M.S. in Computer Science, and Ph.D. in Statistics from the University of Wisconsin-Madison. His research focuses on advancing statistical and machine learning methods for solving business problems in revenue management and pricing. His interests include copula-based dependence modeling, functional data analysis, portfolio optimization, and large-scale forecasting. His research has been funded by the National Science Foundation and has been published in top journals such as Journal of the Royal Statistical Society - Series B, Journal of Machine Learning Research, Journal of Econometrics, and INFORMS Journal on Computing. He was awarded an Honorable Mention for the American Statistical Association Zellner Thesis Award in 2019 and was a finalist for the INFORMS Workshop on Data Science best paper award in 2020. Dr. Zhao also worked as a research intern at the core Data Science team at Google and Target.
Federated Learning for the Hospitality Industry: A case of study for Oracle HGBU Nor1 upsell Cloud Service
Demand estimation with missing data on covariates: Imputation-based approach