On the afternoon of August 10, 2024, at the invitation of the School of Management, Associate Professor Pan Kai from the Hong Kong Polytechnic University delivered an academic report titled "Optimized Dimensionality Reduction for Moment-based Distributionally Robust Optimization" in the Lecture Hall 403 of the School of Management. This lecture was the 21st session of the 2024 Overseas Lecture Series of the School of Management, chaired by Professor Li Jiliu, with the participation of some young faculty members and students from the school.

During the lecture, Pan Kai introduced the moment-based Distributionally Robust Optimization (DRO) framework, which combines statistical information with traditional optimization methods to make decisions under the worst-case distribution scenario. Although DRO problems can be formulated as Semi-Definite Programming (SDP) problems, high-dimensional SDP computations still pose challenges. To address this, Pan Kai proposed an innovative Optimized Dimensionality Reduction (ODR) method. Unlike existing methods, ODR significantly simplifies the problem by reducing the dimension of random parameters while maintaining the robustness of the solution. Finally, Pan Kai demonstrated the advantages of the ODR method in the newsvendor problem to the faculty and students, showing that it outperforms existing methods in both computation time and solution quality.

At the end of the sharing session, Li Jiliu provided a summary for the lecture. Pan Kai patiently answered questions from the faculty and students present, creating a lively atmosphere. The attendees expressed their gratitude to Pan Kai's sharing with warm applause, and the lecture concluded successfully.

Written by Hu Yaping, Reviewed by Shao Jing and Zhang Shuang

Introduction to the Speaker:

Pan Kai is currently an Associate Professor at the Hong Kong Polytechnic University School of Business and the Program Director of the Master of Science in Operations Management. He also serves as the Secretary/Treasurer of the INFORMS Computing Society. His research interests include stochastic optimization, discrete optimization, robust optimization, data-driven methodologies, dynamic programming, and their applications in energy markets, smart cities, supply chains, shared mobility, and transportation. He has worked as a Supply Chain Optimization Research Scientist at Amazon (Seattle, Washington) and as a Power Market Operations Power Systems Engineer at GE Grid Solutions (Redmond, Washington). His research has been published in journals such as Operations Research, Manufacturing and Service Operations Management, INFORMS Journal on Computing, Production and Operations Management, IISE Transactions, European Journal of Operational Research, IEEE Transactions on Power Systems, Transportation Research Part B, and he is the first-place recipient of the 2017 IISE Pritsker Doctoral Dissertation Award.