Speaker: Yan Zhenzhen, Associate Professor at NanyangTechnological University
Time: 16:00 p.m., January 5, 2026
Venue: Room 109, Buidling 12, Wushan Campus
Biography
Dr. Yan Zhenzhen is an Associate Professor at the School of Physical and Mathematical Sciences, with a courtesy appointment at Nanyang Business School, Nanyang Technological University. She currently serves as Assistant Dean (Student Affairs) of the School of Physical and Mathematical Sciences at NTU and as President of the Operational Research Society of Singapore. Her research interests mainly focus on the interplay between optimization and data analytics. She is keen to solve various operations management problems and engineering problems from the distributionally robust perspective, including supply chain design and operations, e-commerce operations, and healthcare operations. She is also particularly interested in data-driven pricing problems and sequential decision making problems. Dr. Yan has published more than ten papers in top-tier journals in the field of operations management, including Management Science, Operations Research, MSOM and POMS.
Abstract:
A mixture of multinomial logits (mixed multinomial logit (MMNL)) generalizes the multinomial logit model, which is commonly used in modeling market demand to cap- ture consumer heterogeneity. Although extensive algorithms have been developed in the lit- erature to learn MMNL models, theoretical results are limited. Built on the Frank-Wolfe (FW) method, we propose a new algorithm that learns both mixture weights and component- specific logit parameters with provable convergence guarantees for an arbitrary number of mixtures. Our algorithm utilizes historical choice data to generate a set of candidate choice probability vectors, each being close to the ground truth with a high probability. We further provide a sample complexity analysis to show that only a polynomial number of samples is required to secure the performance guarantee of our algorithm. Finally, we apply the learned MMNL to data-driven multiproduct pricing problems and quantify how the estimation errors affect the pricing optimality under our proposed data-driven pricing framework Numerical studies are conducted to evaluate the performance of the proposed algorithms.
