17 Jul 2025 (Tue) 17:00-18:00 | Seminar by Prof. Sadan Kulturel‐Konak

Enhancing Fairness in Food Assistance: A Data-Driven Framework for Equitable Resource Distribution

Speaker:

Prof. Sadan Kulturel‐Konak

The Pennsylvania State University

Date:    Jul 17, 2025 (Thursday)

Time:   5:00 pm – 6:00 pm

Venue:  Room 8-28, 8/F Haking Wong Building, The University of Hong Kong

Abstract

This study proposes a data-driven framework to improve fairness in food assistance programs in the United States, focusing on addressing geographic and accessibility inequities. The framework integrates spatial analysis, optimization techniques, and stakeholder engagement to ensure equitable allocation of food resources, especially for communities with limited transportation access. It utilizes granular, location-specific data to identify inefficiencies in current distribution systems and to support more informed decision-making. To promote usability across a broad range of stakeholders, including food banks, community organizations, volunteers, and beneficiaries, the framework includes tailored visualizations and tools that account for varying levels of technical expertise. Designed to operate at a highly localized scale, the framework prioritizes underserved communities and incorporates feedback mechanisms to support continuous improvement. This adaptable and collaborative approach aims to transform food distribution systems into more equitable and resilient networks, ultimately contributing to long-term food security.

Bio

Sadan Kulturel‐Konak is a Professor of Management Information Systems and the Director of the Flemming Creativity, Entrepreneurship, and Economic Development (CEED) Center at Penn State Berks. Dr. Kulturel also has a courtesy appointment at Penn State Harold and Inge Marcus Department of Industrial and Manufacturing Engineering. She received her Ph.D. in Industrial and Systems Engineering from Auburn University. Dr. Kulturel’s research focuses on modeling and optimizing complex systems using hybrid approaches combining heuristic methods and exact techniques from probability and operations research. The primary application areas of her research include designing and redesigning facilities to provide significant economic benefits for US firms. Dr. Kulturel is also interested in pedagogical research related to entrepreneurship and STEM fields, including professional skill development, innovative thinking skills, and gender differences in learning styles. She served as the elected president of INFORMS-Women in OR/MS (WORMS), the elected chair of INFORMS- Facility Logistics Special Interest Group, and the chair of the ASEE Middle Atlantic Section. She is currently an academic member of the College Industry Council on Material Handling Education (CICMHE). She is an Associate Editor of the Engineering Applications of Artificial Intelligence (Elsevier). She has served as a principal investigator on several sponsored projects funded by the National Science Foundation (NSF) and VentureWell. She is a member of INFORMS, IEEE, and ASEE.

Student Seminar by Mr. Runqiu Hu on Jun 30, 2025 2PM

Title: Repositioning in bike sharing systems with broken bikes considering on-site repairs

Speaker: Mr. Runqiu Hu (Department of Civil Engineering)

Date: Jun 30, 2025 (Monday)

Time: 2:00 pm - 3:00 pm

Venue: Room 612B, 6/F, Haking Wong Building, The University of Hong Kong

About the talk: This research introduces a novel approach to bike-sharing system operations by simultaneously considering both vehicle-based repositioning and on-site repairs of broken bikes. While existing studies have typically assumed broken bikes can only be repaired at depots after collection, this research recognizes that bike-sharing operators also dispatch repairers for on-site repairs, satisfying demand without vehicle repositioning. A mixed-integer linear programming model is developed for a static bike repositioning problem combining vehicle-based delivery/collection with labor-based on-site repairs, aimed at minimizing the total cost of user dissatisfaction and carbon emissions within a specified time budget. For efficient solution of this complex problem, a hybrid algorithm is proposed incorporating Genetic Search with Adaptive Diversity Control and a novel Station Budget Constrained heuristic, which limits time spent at each station based on benefit-cost ratios. Computational experiments demonstrate that the algorithm obtains optimal solutions for small instances and outperforms commercial solvers on larger networks with less computational time. The cost-effectiveness of deploying repairers is examined, revealing diminished effectiveness with longer repair times and lower percentages of broken bikes. These findings highlight the need for dynamic repairer allocation based on the system's actual damage level, suggesting preventive maintenance strategies can reduce both broken bikes and repair time. The study contributes to the understanding of how on-site repairs can be integrated with traditional repositioning methods in bike-sharing systems.

About the speaker: Mr. Runqiu Hu is currently a Ph.D. student in the Department of Civil Engineering at The University of Hong Kong, supervised by Prof. W.Y. Szeto. He received his bachelor’s degree in Cybersecurity from the Department of Computer Science, Nanjing University of Posts and Telecommunications in 2018 and master’s degree in Computer Technology from the School of Computer Science and Engineering, Southeast University, China, in 2021. His research interests include shared mobility systems, transportation optimization, multi-objective optimization, and the application of artificial intelligence in transportation engineering. Runqiu has publications in several journals including Transportation Research Part E, Transportation Research Part D, IEEE Internet of Things Journal, and IEEE Access. His work focuses on bike-sharing system operations, electric vehicle range anxiety analysis, and intelligent transportation optimization. During his master’s study, he developed expertise in knowledge representation and reasoning, particularly in non-monotonic reasoning for decision support systems using answer set programming and finding reasoning paths for the explainable AI. His current research explores the integration of operations research techniques with transportation engineering challenges, with a specific emphasis on developing efficient algorithms for complex optimization problems in shared mobility contexts.

18 Jun 2025 (Wed) 15:00-16:00 | Seminar by Dr. Fan ZHU & Mr. Feifei SU (Baidu Apollo)

Apollo Go: Driving the Future with Innovation

Speaker:

Dr. Fan ZHU & Mr. Feifei SU

Baidu Apollo International

Date:    Jun 18, 2025 (Wed)

Time:   3:00 pm – 4:00 pm

Venue:  Room 612B, 6/F Haking Wong Building, The University of Hong Kong

Abstract

Baidu Apollo’s robotaxi marks a significant step in the advancement of autonomous driving and intelligent mobility. The seminar aims to provide an overview of how autonomous driving is becoming integrated into daily life and its potential to enhance transportation systems. The progress and vision behind Apollo Go’s robotaxi service, along with the technologies supporting its development, will also be introduced.  The discussion will also highlight future trends in autonomous vehicle technology and Baidu Apollo’s globalization strategy, with Hong Kong identified as one of the first international destinations. By sharing these developments, Baidu Apollo seeks to encourage thoughtful conversation about the role of innovation in shaping the future of urban transportation and the ways these advancements could benefit communities in Hong Kong and beyond.

About the Speakers

Dr. Fan ZHU is the Principal Architect of Baidu's Intelligent Driving Group (IDG). With over a decade of experience in artificial intelligence and autonomous driving, he brings extensive research and project expertise to the field. After earned both his master's and PhD degrees from the University of Edinburgh, UK, he completing his postdoctoral fellowship at the University of Michigan. In 2015, he joined Baidu's US division and became a founding member of Baidu IDG. Fan has published more than 20 papers in prestigious journals such as Nature Methods, Nature Communications, and Bioinformatics. Additionally, he holds over 170 US patents and more than 110 patents granted in China.

SU Feifei is Baidu Apollo’s Senior Product Manager and Technology Evangelist. SU is a Master graduate from Flinders University, Australia, a member of the Data Systems and Simulation Committee of the China Simulation Federation, as well as a member of the Artificial Intelligence and Robotics Committee of the China Education Development Strategy Society.  He serves as an external innovation mentor at several universities, including Beijing Institute of Technology, Jilin University, University of Science and Technology Beijing, Yanshan University etc.. SU is also an external Masters Degree supervisor at Beijing Technology and Business University and Chongqing University of Arts and Sciences.  Currently, SU is mainly responsible for building the developer ecosystem for Baidu Apollo’s Autonomous Driving Open Platform.  He has participated in the compilation of several teaching materials, including “Data Communication and Network Technology”, “Robot Operating System”, “Introduction to Intelligent Connected Vehicles”, “Intelligent Connected Vehicle Perception Technology” and “Intelligent Connected Vehicle Integration and Testing”.