YuCCA Lab

Welcome to the Yu’s Cognitive Computing and Automation Lab (YuCCA Lab) at the University of California, Merced!

Research Focus

The YuCCA Lab, led by Dr. Xiaofan Yu, conducts research on efficient and autonomous AI systems that bring intelligent computing to real-world applications. Our research spans the full stack: from designing efficient AI algorithms and embedded computing platforms to deploying intelligent systems in real-world applications. By co-designing hardware, systems, and AI, we build autonomous edge platforms that can perceive, reason, and act under stringent computing, memory, and energy constraints.

Active Projects

Multimodal Large Language Models for Autonomous Systems

Developing next-generation autonomous systems powered by Vision Language Models (VLMs) that can understand and act on multimodal sensor inputs such as images, LiDAR, radar and IMUs. These projects aim to reduce LLM hallucinations when intepreting sensor data and improving the overall efficiency of system operation.

Publications: IROS'26, WACV'26, IMWUT'25, SenSys'25, MASS'25

Intelligent Agricultural Robotics Systems

Designing intelligent and efficient embedded systems for real-world agricultural applications. These projects aim to bridge the gap between modern AI technologies and practical farming needs, enabling autonomous robots to assist with tasks such as crop monitoring, harvesting, and field management.

Publications: arXiv'25

Hyperdimensional Computing: Algorithms, Systems and Hardware

Advancing neuromorphic and cognitive-inspired computing paradigms (e.g., Hyperdimensional Computing) for next-generation efficient AI on emerging systems and hardware. These projects focus on bridging the gap between Hyperdimensional Computing and real-world cyber-physical applications, developing full-stack solutions that span algorithms, systems, and hardware, including Processing-in-Memory (PIM) technologies.

Publications: DAC'26, AAAI'25, DATE'25, TIOT'25, IPSN'24, DATE'24, TCASAI'24, ASP-DAC'24

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