Invited seminars

  • Two Empirical Studies on Disruptive Innovation, Florida State University, 2022
  • Two Empirical Studies on Disruptive Innovation, HPE Data Science Institue, 2022
  • Managerial Bias in Revenue Management, University of Florida, 2022
  • Managerial Bias in Revenue Management, The Hong Kong Polytechnic University, 2022
  • Two Empirical Studies on Disruptive Innovation, University of Science and Technology of China, 2021
  • Two Empirical Studies on Disruptive Innovation, Ball State University, 2021
  • Two Empirical Studies on Disruptive Innovation, Tsinghua University, 2021
  • Revenue management of online platforms, Duke University, Durham, NC, 2020
  • AI and procurement, University of Nebraska–Lincoln, Lincoln, NE, 2020
  • AI and procurement, Rutgers University, Newark, NJ, 2020
  • AI and procurement, University of Hong Kong, Hong Kong, 2020
  • Last-mile commute: Impact of bike-sharing on restaurants, Drexel University, Philadelphia, PA, 2020
  • Beneficial product returns, Penn State University, University Park, PA, 2018
  • Beneficial product returns, New York University, Shanghai, 2017
  • Beneficial product returns, Baruch College, New York, NY, 2017
  • Overconfident competing newsvendors, Michigan State University, East Lansing, MI, 2016
  • Overconfident competing newsvendors, University of Kansas, Lawrence, KS, 2016
  • Overconfident competing newsvendors, Tsinghua University, Beijing, 2016
  • Product returns in distribution channels, University of Pennsylvania, Philadelphia, 2015

Conference presentations

  • Last-mile commute and food service, INFORMS, Seattle, 2019
  • Overconfident distribution channels, INFORMS, Phoenix, 2018
  • Estimating the discounting factor of dynamic programming with a field experiment, INFORMS, Phoenix, 2018
  • Forecast information sharing with multiple suppliers: Trust & coordination, INFORMS, Phoenix, 2018
  • Estimating the discounting factor of Dynamic Programming with a field experiment, INFORMS Marketing Science, Philadelphia, 2018
  • Dynamic pricing to explore markets with customer- and time-heterogeneity, POMS, Houston, 2018
  • Beneficial consumer returns, POMS, Houston, 2018
  • Dynamic pricing to explore markets with customer- and time-heterogeneity, INFORMS, Houston, 2018
  • Returns policies in distribution channels, POMS, Seattle, 2017
  • Manufacturer competition and product returns, POMS, Seattle, 2017
  • Dynamic selling mechanisms for exploring markets with customer- and time-heterogeneity,
  • INFORMS, Nashville, 2016
  • Returns policies for overstock and consumer returns in distribution channels, INFORMS, Nashville, 2016
  • Dynamic pricing to explore markets with customer- and time-heterogeneity, POMS, Orlando, 2016
  • Returns policies for overstock and consumer returns in distribution channels, POMS, Orlando, 2016
  • Overconfident competing newsvendors, INFORMS, Philadelphia, 2015
  • Demand uncertainty reduction effects in decentralized supply chains, POMS, D.C., 2015
  • Dynamic pricing of vertically differentiated products with unknown customer heterogeneity, POMS, D.C., 2015
  • Product returns in distribution channels, POMS, D.C., 2015
  • Overconfident competing newsvendors, POMS, D.C., 2015
  • Competing with bandit supply chains, DSI, San Francisco, 2012
  • Competing with bandit supply chains, INFORMS International, Beijing, 2012
  • Competing with bandit supply chains, INFORMS, Phoenix, 2012

Thesis/Dissertation

  • Ph.D. Dissertation, 2013. “Inventory models with incomplete information,” The University of Texas at Dallas, Richardson, Texas, USA (Advisors: Alain Bensoussan and Suresh Sethi).