赵倞同
- 办公室：明德主楼617B
- 职称/职务：讲师
- 办公电话：
- 所属教研室：计量与数量经济学系-数量经济学教研室
- 电子邮箱：zhaojingtong@ruc.edu.cn
- 研究领域：Revenue Management, Business Analytics, Algorithms, Machine Learning, Healthcare Operations
Education
Ph.D. Operations Research with Specialization in Data Science, Columbia University, Aug 2021, New York USA
B.S. Financial Engineering, Columbia University, May 2016, New York USA
B.S. Financial Engineering, Columbia University, May 2016, New York USA
Employment
Assistant Professor, School of Economics, Renmin University of China, Sep 2021 – Present.
Publications
1. Ziwei Wang, Jie Song, Jingtong Zhao. Dynamic ranking of physicians in online healthcare platforms with multiple service types, City, Society, and Digital Transformation: Proceedings of the 2022 INFORMS International Conference on Service Science, Cham: Springer International Publishing, 217-234, 2022.
2. Jingtong Zhao, Hanqi Wen. Dynamic planning with reusable healthcare resources, Flexible Services and Manufacturing Journal, 1-20, 2021, doi: 10.1007/s10696-021-09411-0.
3. Jingtong Zhao. Synergy between customer segmentation and personalization, Journal of Systems Science and Systems Engineering, 30(3), 276-287, 2021.
4. Xin Pan, Jie Song, Jingtong Zhao, Van-Anh Truong. Online contextual learning with perishable resources allocation. IISE Transactions, 52(12), 1343-1357, 2020.
5. Ward Whitt, Jingtong Zhao. Many‐server loss models with non‐poisson time‐varying arrivals. Naval Research Logistics (NRL), 64(3), 177-202, 2017.
6. Andrew Li, Ward Whitt, Jingtong Zhao. Staffing to stabilize blocking in loss models with time-varying arrival rates. Probability in the Engineering and Informational Sciences, 30(2), 185-211, 2016.
2. Jingtong Zhao, Hanqi Wen. Dynamic planning with reusable healthcare resources, Flexible Services and Manufacturing Journal, 1-20, 2021, doi: 10.1007/s10696-021-09411-0.
3. Jingtong Zhao. Synergy between customer segmentation and personalization, Journal of Systems Science and Systems Engineering, 30(3), 276-287, 2021.
4. Xin Pan, Jie Song, Jingtong Zhao, Van-Anh Truong. Online contextual learning with perishable resources allocation. IISE Transactions, 52(12), 1343-1357, 2020.
5. Ward Whitt, Jingtong Zhao. Many‐server loss models with non‐poisson time‐varying arrivals. Naval Research Logistics (NRL), 64(3), 177-202, 2017.
6. Andrew Li, Ward Whitt, Jingtong Zhao. Staffing to stabilize blocking in loss models with time-varying arrival rates. Probability in the Engineering and Informational Sciences, 30(2), 185-211, 2016.
Selected Presentations
1. “Bandits with Correlated Contexts: Application to Product Ranking under Evolving Consumer Reviews”, The 2nd “Data Intelligence and Management” Conference, Changsha, Nov 2022.
2. “Pricing and Ranking under a Cascade Model of Consumer Review Browsing”, INFORMS RM&P, Virtual, Jun 2021.
3. “Learning to Rank under Evolving Consumer Reviews”, INFORMS, Seattle, Oct 2019.
4. “Online Contextual Learning with Perishable Resources Allocation”, INFORMS Healthcare, MIT, Jul 2019.
5. “Staffing to Stabilize Blocking in Loss Models with Time-Varying Arrival Rates”, INFORMS, Nashville, Nov 2016.
2. “Pricing and Ranking under a Cascade Model of Consumer Review Browsing”, INFORMS RM&P, Virtual, Jun 2021.
3. “Learning to Rank under Evolving Consumer Reviews”, INFORMS, Seattle, Oct 2019.
4. “Online Contextual Learning with Perishable Resources Allocation”, INFORMS Healthcare, MIT, Jul 2019.
5. “Staffing to Stabilize Blocking in Loss Models with Time-Varying Arrival Rates”, INFORMS, Nashville, Nov 2016.
Honors and Awards
1. Coauthor of the Paper that Won Best Student Paper Award, INFORMS International Conference on Service Science, 2022
2. Honorable Mention for Best Paper Award, IISE Transactions Focus Issue on Operations Engineering and Analytics, 2021
3. Stephen D. Guarino Memorial Award, Columbia University, 2016
4. Summa Cum Laude, Columbia University, 2016
5. Dean’s List, Columbia University, Fall 2012 – May 2016
2. Honorable Mention for Best Paper Award, IISE Transactions Focus Issue on Operations Engineering and Analytics, 2021
3. Stephen D. Guarino Memorial Award, Columbia University, 2016
4. Summa Cum Laude, Columbia University, 2016
5. Dean’s List, Columbia University, Fall 2012 – May 2016
Teaching
1. Operations Research & Big Data (undergraduate course), Spring
2. Data Analysis & Machine Learning (graduate course), Fall
3. Stochastic Processes (graduate course), Spring
2. Data Analysis & Machine Learning (graduate course), Fall
3. Stochastic Processes (graduate course), Spring
Skills
• Python, Matlab, C, C++, R, Java, Microsoft (Word, Excel, PowerPoint).
• Machine learning, Reinforcement learning, Deep learning.
• French (intermediate), Spanish (intermediate)
• Machine learning, Reinforcement learning, Deep learning.
• French (intermediate), Spanish (intermediate)