CITRIS Awards - Zhaodan Kong and Lee Miller

Center Faculty Selected for CITRIS 2022 Seed Awards

By Noah Pflueger-Peters

Original articles published here (by Noah Pflueger-Peters) and here (by Dateline Staff).

Center for Information Technology Research in the Interest of Society and the Banatao Institute (CITRIS) at the University of California (UC) recently announced the 2022 CITRIS Seed Awards recipients. The eight selected proposals, submitted by multicampus teams from Berkeley, Davis, Merced and Santa Cruz, will receive up to $60,000 for their work, thanks in part to external philanthropic support. 

The winning projects, which are designed to show results within one year, address various challenges in such areas as aviation, climate resilience, digital health and robotics.  

Two of the eight funded projects feature faculty from the UC Davis Center for Neuroengineering & Medicine, including Mechanical and Aerospace Engineering (MAE) Associate Professor Zhaodan Kong and Neurobiology, Physiology and Behavior (NPB) Professor Lee Miller. Professors Kong and Miller are members of the center’s Steering Committee.

The funded projects are:

  • Restoring Speech Communication With a Multimodal Decoder-Synthesizer — Lee Miller (lead PI), professor, Department of Neurobiology, Physiology and Behavior, College of Biological Sciences; and Daniel Cates, assistant professor, Department of Otolaryngology, UC Davis Health. Partner campus: UC Merced. This project will develop an assistive device that combines recordings of a person’s facial expressions and muscle movements and uses neural networks to synthesize and produce fluent speech in their own voice.
  • Trust Aware Human-Machine Teaming Using Real-Time Neurophysiological Data — Zhaodan Kong, associate professor, Department of Mechanical and Aerospace Engineering. Partner campus: UC Berkeley. This project will develop a real-time measurement of human-machine trust by recording physiological signals in the brains of experiment participants as they interact with high- and low-performing robots in a tool-sorting task.

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