Y. (Yongqi) Dong, MSc

Y. (Yongqi) Dong

Pure profile

Contact

Y.Dong-4@tudelft.nl

Room: 23.HG 4.41

Profile

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 C, IEEE 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).

Research interests

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.

More research interests