Shujie Luan
sjluan@seu.edu.cn
🔈I am now on the 2024-2025 job market.Â
Hi, my name is Shujie and I am currently a final-year Ph.D. candidate at Southeast University, where I am fortunate to be supervised by Prof. Weili Xue. I spent two years of my Ph.D. at Johns Hopkins University, where I had the privilege of being mentored by Prof. Ruxian Wang, Prof. Tinglong Dai, Prof. Shubhranshu Singh and Prof. Ozge Sahin. Additionally, I collaborated on projects at the Chinese University of Hong Kong, Shenzhen, during a three-month visit.Â
News
2025.02.20: Our paper "Pull-Push Strategies under Cournot Competition" has been accepted by the Omega journal.Â
2024.08.19: Our paper "Joint Assortment and Price Optimization with Multiple Purchases" has been accepted by the prestigious FT50 and UTD24 journal Production and Operations Management. We propose a multi-purchase choice model that can outperform existing multi-product multi-unit models in both fitting and predicition, as validated by JD.com data. We also introduce a fast and efficient algorithm for a joint assortment and price optimization problem, offering significant managerial insights.Â
Education
Johns Hopkins University 2022.07 – 2024.07
Visiting Ph.D. Student
Mentors: Tinglong Dai, Ruxian Wang, Shubhranshu Singh, Ozge Sahin
The Chinese University of Hong Kong, Shenzhen 2022.03 – 2022.06
Visiting Ph.D. Student
Southeast University 2020.04 – 2025.06 (Expected)
Ph.D. Student in Management Science and Engineering
Advisor: Weili Xue
Southeast University 2017.09 – 2020.03
Major: Logistics Engineering
Nanjing University of Aeronautics and Astronautics 2013.09 – 2017.06
B.S. in Information Management and Information System (Ranked 1st)Â
Research Interests
Artificial Intelligence, Human-AI Interaction, Algorithmic Bias;
Consumer Choice Models, Assortment Optimization, Revenue Management and Pricing;
Marketing-Operations Interfaces;
Data Analytics & Empirical Analysis, Statistical Modelling, Algorithm Design
Teaching ExperienceÂ
Johns Hopkins University, Teaching Assistant (Fall 2022, Fall 2023)
Data Analytics