Linshen Liu is a PhD student in the Department of Civil and Systems Engineering (CaSE) at Johns Hopkins University and a member of the Center for Systems Science and Engineering (CSSE). He is supported by the Joseph Meyerhoff Fellowship and works at the intersection of autonomous driving, edge-device computing, and efficient multimodal large language model.
His research focuses on designing hardware-aware machine learning systems for 3D perception, including Mixture-of-Experts architectures, spiking–artificial hybrid neural systems, and multi-agent framework. He is particularly interested in developing scalable, generalizable sensing–computation pipelines that enable safe and efficient autonomous mobility in complex urban environments.
His long-term goal is to advance next-generation intelligent transportation systems through efficient AI models, cross-vehicle sensing integration, and city-scale embodied intelligence.
With the rapid advancement of deep learning technology, feature extraction backbones that are effectively trained have found increasing use in various traffic perception tasks, such as vehicle recognition and roadway […]
Autonomous vehicles, open-world robots, and other automated systems rely on accurate, efficient perception modules for real-time object detection. Although high-precision models improve reliability, their processing time and computational overhead can […]