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Coowa Appoints Yang Xue Chief Scientist, Launches SJTU Lab

Summarized by NextFin AI
  • Coowa appointed Yang Xue as chief scientist to lead a new laboratory focused on embodied intelligence and world models, enhancing academic collaboration.
  • Yang Xue has a strong background in computer vision and embodied AI, with over 10,000 citations and recognition as a leading researcher.
  • The laboratory will utilize Coowa's fleet of over 10,000 robots and extensive data to improve data-engineering efficiency and advance AI applications.
  • Coowa, founded in 2015, operates in over 50 cities, developing AI solutions for sanitation, transportation, and property services.

NextFin News — Coowa appointed Shanghai Jiao Tong University assistant professor Yang Xue as chief scientist on August 3 and will co-establish a university-level Embodied Intelligence and World Model Joint Laboratory with the university.

Yang Xue, a tenure-track assistant professor and Ph.D. supervisor at SJTU’s School of Automation and Intelligent Sensing, previously served as a researcher at Shanghai AI Laboratory’s OpenGVLab. He earned his doctorate at SJTU under Professor Junchi Yan, specializing in computer vision, multimodal large models, spatial intelligence and embodied AI, with more than 10,000 Google Scholar citations, continuous inclusion among the world’s top 2% scientists and recognition as an Elsevier highly cited Chinese researcher. The laboratory, which Yang will direct from June 2026 to May 2029, will draw on Coowa’s deployed fleet of more than 10,000 robots and 50 petabytes of physical-world data to raise data-engineering conversion efficiency, accelerate upgrades to the company’s COOWAM embodied brain and advance large-scale application of world models.

Coowa, formally Coowa Robot and founded in 2015, develops city-scene embodied AI solutions spanning sanitation robots, autonomous buses and property-service platforms, with commercial operations already running in more than 50 cities. The dual appointments strengthen the firm’s academic pipeline as it scales its dual-layer world-action model architecture for real-world physical agents.

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Insights

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