Byron Yu is a professor in the Departments of Electrical and Computer Engineering and Biomedical Engineering at Carnegie Mellon University. Yu received a B.S. degree in Electrical Engineering and Computer Sciences from the University of California, Berkeley in 2001. He received M.S. and Ph.D. degrees in Electrical Engineering in 2003 and 2007, respectively, from Stanford University. From 2007 to 2009, he was a postdoctoral fellow jointly in Electrical Engineering and Neuroscience at Stanford University and at the Gatsby Computational Neuroscience Unit, University College London.
Yu joined the faculty of Carnegie Mellon University in 2010, where he is an associate professor in the Departments of Electrical and Computer Engineering and Biomedical Engineering and the Gerard G. Elia Career Development Professor. He is broadly interested in how large populations of neurons process information, from encoding sensory stimuli to driving motor actions. His group develops and applies novel statistical algorithms and uses brain-computer interfaces to study brain function.
Byron Yu: Neuroscience and Engineering
Brain Computer Interfaces Helping People with Disabilities
Yu weighs in on the benefits of brain computer interfaces
BME/ECE’s Byron Yu weighs in on the benefits of brain computer interfaces (BCI) in a BuiltIn explainer piece. “Our findings will eventually lead to methods to help people learn everyday skills more quickly and to a higher level of proficiency.”
It takes two: analyzing neural activity from calcium imaging
Biomedical engineering researchers analyzed existing methods that are used to interpret calcium imaging recordings, and proposed a novel method that combines two leading approaches.
Novel method aims to demystify communication in the brain
Researchers from Carnegie Mellon University, Einstein College of Medicine, and the Champalimaud Foundation introduce a new statistical method, DLAG, to detangle communication across brain areas.
Disentangling interactions across brain areas
CMU researchers are simultaneously recording populations of neurons across brain areas in the visual system and utilizing novel statistical methods to observe neural activity patterns being conveyed.
Does the brain learn in the same way that machines learn?
A new perspectives piece co-authored by Carnegie Mellon University researchers relates machine learning to biological learning.
Three-million dollar grant to fund study of internal states in the brain
Steve Chase, Matt Smith, and Byron Yu were recently awarded a $3 million grant from the NSF to support research investigating internal states in the brain, including motivation, attention, and arousal, using brain-computer interfaces.
Take two: Integrating neuronal perspectives for richer results
Carnegie Mellon University researchers have identified a way to bridge two neuronal approaches traditionally used in isolation, resulting in a richer understanding of neuronal activity.
Yu quoted on brain-computer interfaces
BME/ECE’s Byron Yu was quoted on Axios about his brain-computer interface research.
Connecting the dots between engagement and learning
New research from Carnegie Mellon University and the University of Pittsburgh examines how changes in internal states, such as engagement, can affect the learning process using BCI technology.
American Institute for Medical and Biological Engineering
Engineering faculty named AIMBE Fellows
BME’s Adam Feinberg, ChemE’s Kathryn Whitehead, BME’s Byron Yu, and BME Associate Department Head Conrad Zapanta have been elected to the American Institute for Medical and Biological Engineering’s College of Fellows.
NIH-funded project will get your attention
Matt Smith and Byron Yu will simultaneously record multiple regions of the brain as subjects go through the process of preparing, establishing, and maintaining attention.
How the brain’s internal states affect decision-making
By recording the activity of separate populations of neurons simultaneously, researchers have gained an unprecedented insight into how the “waxing and waning” of our mental state influences the decisions we make.