Invited seminars
- AI in Operations Management, University of Kent, 2024
- AI in Operations Management, University of Liverpool, 2024
- Physician Adoption of AI Assistant, University of Texas at Dallas, 2024
- AI in Operations Management, Instituto Tecnológico Autónomo de México, 2023
- AI in Operations Management, University of Minnesota, 2023
- Two Empirical Studies on Disruptive Innovation, University of Florida, 2023
- Managerial Behavior in Revenue Management, Texas A&M University, 2023
- Managerial Bias in Revenue Management, University of Miami, 2022
- 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
- Tutorial: AI in Operatoins Management, POM Conference, Orlando, 2023
- AI-driven decision sciences, DSJ Mini-Conference, 2023
- 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).
