Seminar of Asst. Prof. Ziyue LI, 2023年8月18日(星期五)10:00 – 11:00, 舜德楼412教室, 腾讯会议ID:812 309 697,密码:230818 2023.08.13


时间:10:00 – 11:00

主题:Advanced Machine Learning for Smart Transportation

主讲人:Ziyue LI




参加方式(二):腾讯会议ID:812 309 697,密码:230818

讲座介绍:Intelligent Transport Systems (ITS) have been essential in the smart city blueprint. Overall, the universal goal of an ITS is to improve traffic conditions via machine learning and artificial intelligence. But the questions are: what kind of data are we expected from the ITS? What kind of tasks are under the stage spotlight? And what kind of techniques under the “machine learning” umbrella could offer efficient and accurate solutions? In this talk, we will voyage the journey and find the answer together. Throughout the journey, you will see the term “spatiotemporal” or “spatial-temporal” a lot. Spot on! The traffic data is one of the most typical spatiotemporal data. And the core to make our algorithm work well is to capture these spatiotemporal correlations. If we categorize the ITS problem in more detail, we will find out there are three types of domains in ITS. They are (1) Static Data Analysis (e.g., flow, speed, demand), (2) Dynamic Data Analysis (e.g., trajectory), and (3) Traffic Management (e.g., traffic signal control, congestion management). We will cover all these three domains in our lectures. Last, but not least, we will offer abundant resources including famous research papers, public datasets, open-source codes, and so on if you are interested in this research area.

主讲人介绍:Dr. Ziyue LI (Bonald) is an assistant professor in the Information System Department, WiSo Faculty, University of Cologne. He is also the Chief Machine Learning Scientist at EWI. He earned his Data Mining and Machine Learning doctorate at The Hong Kong University of Science and Technology. His research targets high-dimensional data mining and deep learning methodologies for real-world problems, such as tensor, spatiotemporal high-dimensional data, interpretable machine learning, knowledge semantic graphs, topic models, transfer learning, and self-supervised learning. Those methods have been applied in various industries such as smart transportation (mainly), smart manufacturing, and multimedia. Dr. Li has been awarded several paper awards from INFORMS, IISE, and IEEE.

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