Ph.D Student

Yicong Xiao received his Ph.D. from the University of Utah. During his doctoral training, his research focused on invasive BCIs and computational neuroscience. Using the Utah Electrode Arrays, he investigated neural encoding and decoding, neural population dynamics, and the neural mechanisms underlying motor function. His work aimed to understand how neural activity represents behavioral outputs and contributed to the development of BCI systems and neural decoding algorithms. His current research interests center on the dynamic representation of cognition and memory and their underlying neural mechanisms. By integrating multimodal data, including eye tracking, EEG, and intracranial electrophysiology, he studies how cognitive states are formed, updated, and reorganized during natural vision, with the goal of elucidating the dynamic interactions among behavior, cognition, and brain activity.