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, works on impactful research problems to push the boundaries of edge computing and intelligent embedded systems. Our research sits at the intersection of edge AI, embedded systems, and real-world cyber-physical applications. We co-design edge AI algorithms and embedded systems to build intelligent edge platforms that can perceive, reason, and act autonomously in real-world environments despite limited computing, memory, and energy resources.

✨ We are recruiting two Ph.D. students (starting Fall 2026) and multiple Research Assistants (starting anytime).
For more information, please visit Join Us.

Active Projects

Multimodal Large Language Models for Autonomous Systems

Developing next-generation autonomous systems powered by Large Language Models (LLMs) 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: 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

Neuromorphic 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 neuromorphic computing and real-world cyber-physical applications, developing full-stack solutions that span algorithms, systems, and hardware, including Processing-in-Memory (PIM) technologies.

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

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