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Apollo Go: Driving the Future with Innovation

Speaker:

Mr. WANG Ning

Baidu Apollo International

Date:    Mar 4 (Wed), 2026

Time:   5:00pm – 6:00pm

Venue:  Tam Wing Fan Innovation Wing Two, G/F Run Run Shaw Building, HKU



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 speaker

Mr. Ning WANG is a Senior Product Specialist at Baidu Apollo, where he leads the integration and large‑scale deployment of Apollo Go’s 6th‑generation Robotaxi (RT6) and he has played a key role in delivering multiple industry‑first milestones in autonomous mobility.

 

Mr. Wang holds a Master’s degree in Information Technology and Sensor Systems from the Technical University of Darmstadt in Germany. With nearly a decade of R&D and product leadership experience across China and Germany, including prior roles as Product Director at Momenta and Audi AG, he brings deep expertise in autonomous driving algorithms, data closed‑loop systems, and vehicle software integration. At Baidu Apollo, he is committed to bridging cutting‑edge AI research with real‑world, at‑scale industrial applications, advancing the commercialization of robotaxis and next‑generation intelligent driving solutions.




 

 
 
 

[Please scroll down for the English texts]


AI与数据科学暑期工作坊

举办单位: 香港大学交通运输研究所

内容介绍: AI与数据科学暑期工作坊面向所有志在增进自己在AI与数据科学方面理论知识与实践技能的本科生。为期五天的工作坊包含由香港大学多个科系教授主讲的讲座、实践操作和咨询指导、以及科技考察等项目。讲座内容涵盖大数据分析、机器学习、深度学习、大语言模型、预测、最优化、以及科研技能等各个主题。学生需通过笔试和小组项目来取得结业证书。每个小组项目将由一名港大教授指导,并通过小组展示和书面报告的形式进行测评。表现最佳的小组将获颁最佳项目奖。
主讲人:
项目导师(暂定):
司徒惠源教授
司徒惠源教授
郭永鸿教授
李加阳教授
柯锦涛教授
严鑫涛教授
严鑫涛教授
许骏什教授
赵展教授

活动日期: 2026年8月17日(一)至8月21日(五)
地点: 香港大学主校园
活动日程(暂定):

 

8/17
8/18
8/19
8/20
8/21
上午 (9-12)
讲座:大数据分析
讲座:深度学习
讲座:大语言模型与预测方法
讲座:最优化
笔试、小组项目咨询
下午 (2-5)
讲座:机器学习
小组项目咨询
科技考察
讲座:沟通与科研技能
小组展示和结业仪式
报名截止日期: 2026年8月10日
活动名额: 名额有限,先到先得
工作坊学费:
  • 早鸟价9800港币(不晚于2026年4月15日报名和付款,费用不含交通食宿)
  • 普通价11800港币(费用不含交通食宿)
支付方法: VISA、MASTERCARD、微信支付、支付宝
联系邮箱: hkits@hku.hk

[English version of the texts]


AI and Data Science Summer Workshop

Organized by: Institute of Transport Studies, HKU

 

What to expect: This is a 5-day workshop designated for undergraduate students who wish to strengthen their theoretical knowledge and practical skills in AI and data science. In the 5-day workshop, participants will engage in a diverse range of activities, including English lectures by HKU professors from various departments, hands-on sessions with consultation and supervision, and a technical visit. The lectures will cover a broad spectrum of topics in AI and data science, such as big data analytics, machine learning, deep learning, large language models, forecasting, optimization, and research skills. To earn a certificate of completion, participants are required to pass a written test and a group project. Each group project will be supervised by an HKU professor and assessed by a group presentation plus a written report. The group demonstrating the best performance in their project will be presented with the best project award.


Instructors:

Potential project supervisors:

Prof. W. Y. Szeto

Prof. W. Y. Szeto

Prof. Yong-Hong Kuo

Prof. Jiayang Li  

Prof. Jintao Ke

Prof. Xintao Yan

Prof. Xintao Yan

Prof. Junshi Xu

Prof. Zhan Zhao


Dates: Aug 17 – Aug 21, 2026 (Mon - Fri)

Venue: HKU Main Campus

Tentative Schedule:

 

Aug 17 (Mon)

Aug 18 (Tue)

Aug 19 (Wed)

Aug 20 (Thu)

Aug 21 (Fri)

AM Session

(9 am-12 noon)

Lecture: Big data analytics

Lecture: Deep Learning

Lecture: Large language models and forecasting techniques

Lecture: Optimization

Test and consultation hours for group projects

PM Session

(2-5pm)

 

Lecture: Machine learning

 

Consultation hours for group projects

Technical tour: TBD

Lecture: Communication and research skills

Group Presentation and closing ceremony

 

Registration link:

Registration deadline: August 10, 2026

Quota: Limited quota. First-come-first-served.

Tuition fee:

  • Early bird: HK$9,800 (excluding accommodation, transportation, and meals) (registration and payment on or before April 15, 2026)

  • Normal fee: HK$11,800 (excluding accommodation, transportation, and meals)

Payment method: VISA, MASTERCARD, WeChat Pay, Alipay

Payment link:

Enquiries: hkits@hku.hk

 
 
 

Designing Smart and Resilient Transport Systems


(The lecture slides can be downloaded here)


Speaker:

Dr. Beatriz Martinez-Pastor

School of Civil Engineering

University College Dublin

Date:    Dec 17, 2025 (Wednesday)

Time:   5:00 pm – 6:00 pm

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


Abstract

This presentation introduces the core principles of designing smart and resilient transport systems, emphasising how data-driven methods, user-centred approaches, and climate-adaptive strategies can enhance the robustness and inclusivity of future mobility networks. It then highlights three key initiatives. The SETO project advances the smart enforcement of transport operations across the EU, creating a digital platform that integrates data from diverse sources and provides seamless access to regulation-related information. The platform ensures high levels of data security by adopting state-of-the-art industry standards and blockchain technology, enabling an innovative, efficient, consistent, and resilient enforcement support system for multimodal and cross-border contexts. The CAPABLE national project develops a digital platform to assess community capability-based resilience, placing communities at the centre of infrastructure system decision-making and supporting more inclusive and adaptive planning. The NBSINFRA European project applies nature-based solutions to enhance the resilience, sustainability, and social value of transport infrastructure, safeguarding critical urban assets against both natural and human-induced hazards. Together, these projects demonstrate how technological innovation, community-centred methods, and nature-based strategies can be combined to create transport systems that are intelligent, inclusive, and highly adaptable.


About the speaker

Dr. Beatriz Martinez-Pastor is an Assistant Professor in the School of Civil Engineering at University College Dublin (UCD), and the Director of the Centre for Critical Infrastructures Research. Between 2021 and 2025, she was the Director of the Civil Engineering Infrastructure programme at Chang’an-Dublin International College, CDIC, UCD. She completed her Ph.D. at Trinity College Dublin, and her MSc. at the University of Cantabria. Since then, she has specialized in the evaluation of transportation resilience, digital transformation of transport systems, intelligent transport systems, and analysis of complex systems. Dr. Martinez-Pastor has coordinated two Horizon Europe projects, SETO (Smart Enforcement of Transport Operations, €4 million and 14 partners), and the CSA for organising TRA2024 (€1.5 million and 15 partners). Dr. Martinez-Pastor is the PI of a Horizon Europe project, NBSINFRA (€5 million and 17 partners), the PI and Academic leader of STRADA and STRADA2, a European project to develop a Leadership programme for women in engineering, the Co-PI of a SFI project, CAPABLE (Resilience Challenge) and is the co-leader of the transport strand in a large national project, NEXSYS. Dr. Martinez-Pastor has managed over 7 million in research funds and published over 50 papers in peer-review journals and conferences.



 
 
 
© 2023 by Institute of Transport Studies. The University of Hong Kong.
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