Yingfei Wang

Assistant Professor
Department of Information Systems and Operations Management
Foster School of Business, University of Washington
527 PACCAR Hall, Seattle, WA 98195-3226
Email: yingfei (at) uw (dot) edu

My research lies at the intersection of data analytics, statistics, machine learning and management information systems, focusing on decision-making under uncertainty, exploring the ways where efficient information collection influences and improves decision-making strategies, with potential applications as diverse as business analytics, healthcare, e-commerce, recommendation systems, financial data analysis, online auction and revenue management. I'm devoted to solve machine learning challenges to provide efficient business solutions, using technologies from deep learning, graphical models, Bayesian optimization, natural language processing and beyond.

Current Research

Machine learning, recommendation systems, Bayesian optimization, dynamic pricing, e-commerce, online auctions, and healthcare analytics.


Ph.D. (Computer Science), Princeton University, 2012-2017

B.S. (Computer Science), Peking University, China, 2008-2012

B.S. (Econ., double major), Peking University, China, 2009-2012

Selected Publicaitons

  • Y. Wang, K. Reyes, W. Powell, K. Brown and C. Mirkin. Nested batch mode active learning with an application to sequential multi-stage testing in materials science. SIAM Journal on Scientific Computing, 37(3):B361-B381, 2015. [PDF]

  • Y. Wang, C. Wang and W. Powell. The knowledge gradient for sequential decision making with stochastic binary feedbacks. Proceedings of the 33rd international conference on Machine learning (ICML), 2016. [PDF] [slides]

  • Y. Wang, H. Ouyang, C. Wang, J. Chen, T. Asamov and Y. Chang. Efficient Ordered Combinatorial Semi-Bandits for Whole-page Recommendation. AAAI Conference, 2017

  • Y. Wang, H. Ouyang, H. Deng and Y. Chang. Learning online trends for interactive query auto-completion. IEEE Transactions on Knowledge and Data Engineering, 2017.

  • Y. Wang and W. Powell. Finite-time analysis for the knowledge-gradient policy. SIAM Journal on Control and Optimization, 56(2): 1105-1129, 2018 [PDF]

  • Y. Li, K. Reyes, J. Vazquez-Anderson, Y. Wang, L. Contreras and W. Powell. A Knowledge Gradient Policy for Sequencing Experiments to Identify the Structure of RNA Molecules Using a Sparse Additive Belief Model. Informs Journal on Computing, forthcoming. [PDF]

  • Y. Wang and W. Powell. MOLTE: a modular optimal learning testing environment. Technical Report, 2016. [PDF]

Papers Under Review

  • T. Zhou, Y. Wang, L. Yan and Y. Tan. Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach with a Dynamic Discrete-Choice Scheme. under review. [PDF]

  • Z. Zhou, Y. Wang, H. Mamani and D. Coffey. How Do Tumor Cytogenetics Inform Cancer Treatments? Dynamic Risk Stratification and Precision Medicine Using Multi-armed Bandits. under review. [PDF]

  • Y. Wang, JM. Nascimento and W. Powell. Reinforcement Learning for Dynamic Bidding in Truckload Markets: an Application to Large-Scale Fleet Management with Advance Commitments. under review. [PDF]

Professional Services

Reviewer for the following journals:

  • MIS Quarterly, 2017-2018
  • IEEE Transactions on Knowledge and Data Engineering (TKDE), 2015-2016
  • Neurocomputing, 2014-2016
  • Pattern Recognition Letters, 2015

Reviewer for the following conferences:

  • International Conference on Information and Knowledge Management (CIKM), 2016
  • International Conference on Artificial Intelligence and Statistics (AISTATS), 2014
  • ACM International Conference on Web Search and Data Mining (WSDM), 2014

Membership in Professional Societies

  • INFORMS member, 2014-current

Conference Organizing

  • Student organizer of the Conference on Learning Theory (COLT), Princeton University, NJ, 2013