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”.

Student Seminar by Ms. Fangyi Ding on May 23, 2025 10AM

Title: A Graph Deep Learning Model for Station Ridership Prediction in Expanding Metro Networks

Speaker: Ms. Fangyi Ding (Department of Urban Planning and Design)

Date: May 23, 2025 (Friday)

Time: 10:00 am - 11:00 am

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

About the talk: Due to their reliability, efficiency, and environmental friendliness, metro systems have become a crucial solution to transportation challenges associated with urbanization. Many countries have constructed or expanded their metro networks over the past decades. During the planning stage, accurately predicting station ridership post-expansion, particularly for new stations, is essential to enhance the effectiveness of infrastructure investments. However, station-level metro ridership prediction under expansion scenarios (MRP-E) has not been thoroughly explored, as most advanced models currently focus on short-term predictions. MRP-E presents significant challenges due to the absence of historical data for newly built stations and the dynamic, complex spatiotemporal relationships between stations during expansion phases. In this study, we propose a Metro-specific Multi-Graph Attention Network model (Metro-MGAT) to address these issues. Our model leverages multi-sourced urban context data and network topology information to generate station features. Multi-relation graphs are constructed to capture the spatial correlations between stations, and an attention mechanism is employed to facilitate graph encoding. The model has been evaluated through realistic experiments using multi-year metro ridership data from Shanghai, China. The results validate the superior performance of our approach compared to existing methods, particularly in predicting ridership at new stations.

About the speaker: Fangyi Ding is currently a PhD candidate in the Department of Urban Planning and Design at the University of Hong Kong (HKU). She holds a master's degree from Tongji University and a bachelor's degree from the Harbin Institute of Technology. Her research interests lie in transportation demand forecasting, network modeling, and travel behavior analysis, with a particular focus on public transit and shared mobility services. Her work has been published in leading academic journals and conferences, including Transportation Research Part D, the Journal of Transport Geography, the Transportation Research Board (TRB) Annual Meeting, and ACM SIGSPATIAL, among others.

15 Apr 2025 (Tue) 10:30-12:00 | Seminar by Prof. Stein W. Wallace

Modeling with Stochastic Programming

Speaker:

Prof. Stein W. Wallace

Professor of Operational Research

Leader of the Centre for Shipping and Logistics

Centre for Shipping and Logistics, NHH Norwegian School of Economics

Date:    Apr 15, 2025 (Tuesday)

Time:   10:30 am – 12:00 nn

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

Abstract

There are many deep papers on the mathematics and algorithmics of stochastic programming. But why should we, as operations research people interested in transportation and logistics, care? The world is stochastic for sure, but does that imply that we need stochastic models to get good decisions? And if we embark on a genuine application, where real money is involved, what are the modeling questions we need to pose? What are the steps we need to take before we arrive at mathematical and algorithmic challenges?

About the Speaker

Stein W. Wallace is a Professor of Operational Research and leader of the Centre for Shipping and Logistics at NHH Norwegian School of Economics. He received his Dr. Scient degree in informatics from the University of Bergen in 1984. He has earlier held professorships at for example Lancaster University Management School, The Chinese University of Hong Kong, Molde University College and NTNU, as well as visiting positions at for example Business School of Sichuan University in Chengdu, IBM Watson Research in NY, Columbia University, ENP Grenoble, France and The University of Washington. Wallace has published more than 120 papers in internationally leading journals such as Operations Research, Management Science, Production and Operations Management, Transportation Science, Transportation Research A, B and D and E, Mathematical Programming, European Journal of Operational Research, and INFORMS Journal on Computing. He is best known for his work in stochastic programming, in particular the two books Stochastic Programming (with Peter Kall from 1994) and Modeling with stochastic programming (with Alan King from 2012, Second Edition n 2024), but also for work in logistics and energy systems. He has over 14000 citations.

He has been on numerous editorial boards, including INFORMS Journal on Computing (1990-2018). He founded the Norwegian OR Society and has held elected positions in The British OR Society as well as The Society for Transportation and Logistics in INFORMS, The Mathematical Programming Society and EURO.