Y. (Yongqi) Dong

Y. (Yongqi) Dong



Academic Homepage: https://yongqidong.github.io/ 

I got my B.Sc. in Telecommunication from Beijing Jiaotong University, and my M.Sc. in Control Science and Engineering from Tsinghua University, where I also minored in Data Science. During the very past few years, I had trained myself as a researcher in various universities, research institutions and companies, adopting Machine Learning and Data Science methods to transportation research and smart mobility. In January 2020, I started my Ph.D. research career within the SAMEN project, under the supervision of Dr. ir. Haneen Farah and Prof. dr. Bart van Arem. My research focuses on developing data-driven models to expand Automated Vehicles' Operational Design Domain in mixed traffic.


My current research centres around the areas of Automated Vehicles, Smart & Shared Mobility, and Artificial Intelligence. I aim to develop innovative Deep Learning models for Automated Vehicles' sensing and Deep Reinforcement Learning models for Automated Vehicles' controlling, and thus realize Safe, Efficient, and Socially Compliant Autonomous Driving. I have also delved into shared mobility employing big data analytics and machine learning techniques to reveal unique spatial-temporal patterns. My previous works have been published in high-quality top journals and conferences, including Transportation Research Part CIEEE Transactions on Intelligent Transportation Systems, and Computer-Aided Civil and Infrastructure Engineering, as well as IEEE International Conference on Intelligent Transportation Systems  (ITSC) and Transportation Research Board annual meeting  (TRB).

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My ultimate goal is to employ artificial intelligence and interdisciplinary research as tools to shape a better world. For that, I have delved into the transportation domain as the use case. The essence of transportation is to reconcile the spatio-temporal imbalance in the distribution of matter, information and energy, which is all about time and space. Thus, I had attached the utmost importance to the spatial-temporal correlations in my research.

My current research centres around three main pillars:

Deep Learning for sensing and anomaly detecting;

Deep Reinforcement Learning for controlling and decision-making;

Big Data Analytics for spatial-temporal pattern mining.

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