PhD Spotlight: Lluvia “Weijia” Jing, PhD’25, Industrial Engineering
Lluvia “Weijia” Jing, PhD’25, industrial engineering, focused on optimizing global humanitarian supply chains. As part of a multi-year collaboration with the U.S. Agency for International Development (USAID), Jing led the development of advanced models to support decision-making for both pre- and post-disaster planning strategies in regions affected by food insecurity.
Lluvia “Weijia” Jing, PhD’25, industrial engineering, joined Northeastern’s PhD program in 2019 after completing a bachelor’s degree in industrial design and product development jointly offered by the Beijing Institute of Technology and Universitat Politècnica de Catalunya in Spain. She is advised by Ozlem Ergun, distinguished professor of mechanical and industrial engineering. Her doctoral research focused on optimizing global humanitarian supply chains through mathematical modeling, stochastic programming, multi-stage stochastic optimization, and deep learning and machine learning applications. As part of a multi-year collaboration with the U.S. Agency for International Development (USAID), Jing led the development of advanced models to support decision-making for both pre- and post-disaster planning strategies in regions affected by food insecurity. These contributions were integrated into the supply chain optimization tool and delivered to USAID, improving the agency’s ability to plan and respond to humanitarian needs globally.
In parallel, Jing gained industry experience with Northeastern’s LEADERS program—an experiential fellowship blending professional development and advanced research. She completed two internships at Hitachi’s Industrial AI Lab where she proposed and developed frameworks for supply chain resilience and risk-aware decision making. She also contributed to patent-pending models for supplier selection under uncertainty and mapped risk mitigation strategies across industries. Jing completed another project at Fidelity Investment’s AI Center of Excellence, where she worked in a large, production-oriented environment, designing and refining retrieval-augmented generation systems, contributing to modular question-answering pipelines. She also applied best practices in collaborative development such as Git-based workflows and structured code reviews.
Jing presented her research at national conferences including INFORMS and co-authored several peer-reviewed publications, including one in the INFORMS Journal on Applied Analytics. She served as a teaching assistant for graduate-level courses and her research and teaching excellence were recognized with the John and Katherine Cipolla Graduate Merit Award from the Department of Mechanical and Industrial Engineering. She also placed second in the INFORMS Doing Good with Good OR student paper competition. Following graduation, Jing plans to launch a startup to develop advanced AI and data-driven solutions to address critical supply chain challenges in the female-focused products and services industry.