Dr Juan Gu | Packaging design | Best Researcher Award
Graduate student, Michigan State University, United States
Juan Gu demonstrates a strong balance of academic rigor, practical experience, and innovation. Her research on packaging, particularly the use of Artificial Neural Networks (ANN) for box compression strength estimation, is a notable contribution to the field. She has excelled in both individual research and collaborative industry work, displaying leadership and technical skills that have been recognized internationally.
Her ability to secure multiple awards in design competitions further showcases her problem-solving and creative skills in packaging innovation. The combination of her academic achievements, industry experience, and ongoing research projects makes her a highly suitable candidate for the Research for Best Researcher Award.
Education :
Juan holds a Master of Science in Packaging from Tianjin Juan Gu is currently pursuing a Ph.D. in Packaging at Michigan State University, School of Packaging, with an expected graduation in May 2025. She has maintained an impressive GPA of 3.7/4.0. Her research focuses on using Artificial Neural Networks (ANN) for estimating Box Compression Strength (BCS), contributing to advancements in packaging design and material efficiency. Her coursework includes specialized subjects such as Advanced Packaging Dynamics, Polymeric Packaging Materials, Stability & Recyclability of Packaging Materials, and Analytical Solutions to Packaging Design.
University of Science and Technology, China, where she also completed her Bachelor of Science in Packaging. Her Master’s thesis investigated microcapsule wall materials based on citric acid-resistant starch.
EXPERIENCE:
Juan has gained valuable hands-on experience throughout her career in packaging engineering. She worked as a Co-op Packaging Engineer at Eli Lilly and Company in Indianapolis, where she contributed to definitive shipping studies and the optimization of autoinjector packaging structures. She assisted full-time engineers in executing projects and collaborated with vendors to design reusable trays for improved packaging efficiency.
Prior to her time at Eli Lilly, Juan worked as a Packaging Engineer at Shanghai Jiajiashengyu Packaging Science and Technology Co., Ltd., where she analyzed cushioning properties to find optimal materials and led data analysis projects that increased productivity and team efficiency.
At the Beijing Municipal Drug Packaging Material Inspection Institution, Juan played a crucial role in testing and inspecting over 500 food contact materials, contributing to national safety standards. Additionally, she worked as a Sales Assistant at Tianjin HengFeng Packaging Products Co., Ltd., enhancing communication and order management.
Juan’s teaching experience as a Teaching Assistant at Michigan State University has allowed her to support and guide undergraduate students in packaging software, consistently receiving positive feedback for her efforts.
Skills:
Juan is highly proficient in CAD software including Solidworks, Creo, and ArtiosCAD. She also possesses intermediate skills in Photoshop and Python, as well as strong proficiency in Microsoft Office tools. Her skills extend to time management, problem-solving, and communication, which she continuously improves in both academic and professional settings.
Awards:
Throughout her academic and professional journey, Juan has earned numerous accolades, including the 2nd and 3rd prizes in the International Pacific Millennium Transport Package Design Competition, and the Excellent Prize in the Lukka Cup Packaging Innovation Design Competition. She has also been recognized with several scholarships, such as the Society of Manufacturing Engineers Scholarship and the Mark & Catherine Walchak Endowed Scholarship, underscoring her academic excellence and dedication to packaging innovation.
Her impressive work has also led to several publications, including a comparative analysis of ANN architectures for BCS estimation published in the Journal of Korea Society of Packaging Science & Technology, and another under review in the Journal of Applied Science focused on evaluating packaging design relative feature importance using ANN. Juan has presented her research at prestigious conferences, including the 24th IAPRI World Packaging Conference and the 66th Annual Meeting of the Korea Society of Packaging Science and Technology.
Research Focus:
Juan’s research primarily focuses on packaging design optimization using Artificial Neural Networks. Specifically, she works on developing and refining models to predict Box Compression Strength (BCS) and evaluating the importance of packaging design features. Her work aims to reduce material waste and enhance the efficiency and sustainability of packaging systems. By integrating machine learning and advanced analytics into packaging design, Juan’s research contributes to making packaging more sustainable and cost-effective.
Publication :
-
-
Gu, Juan, Frank, Benjamin, & Lee, Euihark. (2023). A Comparative Analysis of Artificial Neural Network (ANN) Architectures for Box Compression Strength Estimation. Journal of Korea Society of Packaging Science & Technology, 29(3), 163-174.
-
Gu, Juan, & Lee, Euihark. (Under review). Evaluating Packaging Design Relative Feature Importance using Artificial Neural Network (ANN). Journal of Applied Science.
-
Juan Gu’s body of work reflects a deep commitment to advancing packaging science through innovative, data-driven approaches. Her research has the potential to influence the packaging industry by enhancing the sustainability and functionality of packaging designs. While she could further broaden her research scope and aim for more interdisciplinary collaborations, her existing contributions place her as a strong contender for the Best Researcher Award. Her practical industry experience combined with her advanced academic research offers a well-rounded skill set, making her an exemplary candidate for the award.
