Ruihao Zeng is a Ph.D. candidate in Transportation Engineering at The University of Sydney, specializing in autonomous vehicles and transportation systems. His research focuses on integrating machine learning, data mining, and augmented reality to improve urban mobility, enhance traffic prediction, and explore the interaction between autonomous vehicles and human drivers. With numerous awards under his belt, his work is contributing to pioneering solutions in transportation technology.
Education 🎓
Ruihao Zeng is currently pursuing a Ph.D. in Transportation Engineering at The University of Sydney (2024-2027), where his thesis is titled Automated Etiquette: Societal Norms, Distributed Intelligence, and Prosocial Algorithms for Autonomous Vehicles. He completed an M.Phil. in the same field (2022-2023), focusing on Online Multi-Object Tracking Using LiDAR. Zeng earned his Bachelor of Engineering in Digital Media Technology from Fujian Normal University, China (2018-2022), graduating with a GPA of 85.1, ranking 1st in his class.
Professional Experience 💼
Ruihao Zeng has held various key roles, including Algorithm and Application Developer at The University of Sydney (May-Dec 2023), where he contributed to the development of augmented reality simulations for transportation systems, notably the Lego ARCity project. As a Casual Academic (Feb 2023 – Present), he leads tutorials and provides grading for transport systems courses. Additionally, he completed an IoT Product Development Internship at Xiamen YesSoft Technology Co., Ltd (Jul-Aug 2019), programming hospital monitoring devices using C++ and Arduino.
Academic Cites 📚
Zeng’s research has been showcased at prestigious conferences such as the Transportation Research Board Annual Meeting (2024) and the TRANSW Symposium (2023). His work has gained recognition in the fields of autonomous vehicle systems, urban traffic management, and the application of machine learning in transportation, earning citations from leading scholars.
Technical Skills ⚙️
Ruihao Zeng’s technical expertise is a key strength in his research. He is proficient in programming languages such as Python, MATLAB, and C++, and skilled in frameworks and tools including Keras, PyTorch, and TensorFlow for deep learning. His specializations include LiDAR-based tracking, machine learning algorithms, and the application of augmented reality to transportation solutions.
Teaching Experience 👩🏫
As a Casual Academic at The University of Sydney, Zeng has gained valuable teaching experience, leading tutorials and grading for courses related to transport systems. His role allows him to engage students with complex technical concepts and guide them in applying their learning to real-world transportation challenges.
Research Interests 🔬
Ruihao Zeng’s research interests are centered around autonomous vehicles, machine learning, and the development of smart cities. His work explores the use of prosocial algorithms and etiquette in human-vehicle interactions, applies machine learning algorithms to traffic prediction and urban mobility optimization, and investigates data-driven solutions for enhancing traffic systems and promoting sustainable urban development.