Awarded in 2020
Home Department: Electrical Engineering
Faculty Advisors: Boris Murmann (Electrical Engineering), Krishna Shenoy (Electrical Engineering), and Jaimie Henderson (Neurosurgery)
Research Title: Efficient Machine Learning Implementations for Implantable Brain-Computer Interfaces
Research Description: Fully-implantable intracortical brain-computer interfaces (iBCIs) have the ability to revolutionize neuroscience and medicine. However, the capability, performance, and robustness of iBCIs are limited by the current state of neural decoding algorithms. Machine Learning (ML) based algorithms outperform state-of-the-art decoders, but they require further optimization to use in fully-implantable iBCIs. Pumiao will combine neuroengineering, ML, integrated circuit design, and clinical translation to investigate a variety of machine learning/neural network algorithms, with area and power constraints in mind, in both offline and online closed-loop studies. She will then design and produce optimized ML decoder hardware to help demonstrate the potential of future fully implantable iBCIs.