Baoyue Zhang

Baoyue Zhang

Ph.D Student

Biography

Baoyue Zhang received her B.S. degree in Computer Science from Dalian University of Technology, Dalian, China, in 2024, where she was mentored by Professor Xin Yang and Qi Xu. She is currently pursuing her Ph.D. at the Institute for Artificial Intelligence and the School of Computer Science, Peking University, Beijing, China, under the supervision of Professor Zhaofei Yu. Her research interests focus on neuromorphic vision and embodied AI, exploring innovative methods to integrate sensory processing with autonomous systems.

Education

Peking University (2024 - Present)

Ph.D. in Computer Science and Technology
School of Computer Science and Technology, Peking University
Research Focus: Neuromorphic Vision and Embodied AI

Dalian University of Technology (2020 - 2024)

B.S. in Computer Science and Technology
School of Computer Science, Dalian University of Technology
Awards: Outstanding Graduate, Dalian City; Outstanding Graduate, Dalian University of Technology

Projects

UAV Circular Obstacle Autonomous Navigation System (2021 - 2022)

Project Lead，Supervised by Professor Xin Yang
This project focuses on developing a system for UAVs to autonomously detect and navigate circular obstacles in both simulated and real-world environments using deep visual perception and intelligent flight control.
Key Achievements:

  • National Champion in the 2022 Robomaster UAV Smart Perception Competition
  • 7th place in the 2021 “Intelligent Flying” UAV Smart Perception Competition (only undergraduate award-winning team)

Dynamic Obstacle Avoidance System for UAVs Using Event Cameras and Spiking Neural Networks (2022 - 2023)

Project Lead，Supervised by Professor Xin Yang
In this project, the dynamic detection of high-speed obstacles was enhanced by leveraging event cameras and deep learning algorithms, including Convolutional Neural Networks (CNN) and Spiking Neural Networks (SNN).
Key Achievements:

  • Second place in the Huawei ICT Competition (National Level)

Research Experience

Real-time Low-latency Detection for Autonomous Robots with a Neuromorphic Spike Camera (2023 - 2025)

Lead Author, Corresponding Author: Zhaofei Yu
This research explores a novel paradigm for autonomous robotic perception using neuromorphic cameras. The system boasts a low-latency response of less than 6 ms, offering over 300 Hz of system response frequency, enabling high-speed machine perception.