Carnegie Mellon University

Byron Yu

Byron Yu

Professor, Electrical and Computer Engineering
Gerard G. Elia Career Development Professor, Biomedical Engineering

Address 5000 Forbes Avenue
Pittsburgh, PA 15213


Byron Yu received the B.S. degree in Electrical Engineering and Computer Sciences from the University of California, Berkeley in 2001. He received the 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. He then joined the faculty of Carnegie Mellon University in 2010, where he is an Associate Professor in Electrical & Computer Engineering and Biomedical Engineering and the Gerard G. Elia Career Development Professor.


Byron Yu's research is at the intersection of neuroscience, engineering, and machine learning.  He is broadly interested in how large populations of neurons process information, from encoding sensory stimuli to driving motor actions. To address basic scientific questions about brain function, his group develops and applies 1) novel statistical algorithms, such as dimensionality reduction and dynamical systems methods, and 2) brain-computer interfaces.


  • Neural signal processing
  • Brain-computer interfaces
  • Statistics and machine learning

Related news

Monday, January 23, 2023

It takes two: Analyzing Neural Activity From Calcium Imaging

Carnegie Mellon University and Howard Hughes Medical Institute researchers teamed up to analyze existing methods that are used to interpret calcium imaging recordings, as well as propose a novel method that combines two leading approaches.
Thursday, March 24, 2022

Disentangling Interactions Across Brain Areas

A long-standing research collaboration between Carnegie Mellon University, Albert Einstein College of Medicine, and Champalimaud Research is simultaneously recording populations of neurons across multiple brain areas in the visual system and utilizing novel statistical methods to observe neural activity patterns being conveyed between the areas.
Tuesday, April 13, 2021

Connecting the Dots Between Engagement and Learning

Byron Yu and Steven Chase together with colleagues from the University of Pittsburgh published their findings in Nature Neuroscience.
Tuesday, September 08, 2020

This 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. This project is funded by the NIH.
Thursday, April 23, 2020

Stabilizing Brain-Computer Interfaces

Researchers from CMU and Pitt have published research in Nature Biomedical Engineering that will drastically improve brain-computer interfaces and their ability to remain stabilized during use, greatly reducing or potentially eliminating the need to recalibrate these devices during or between experiments.
Monday, September 16, 2019

Brain changes when mastering new skills

Mastering a new skill—whether a sport, an instrument, or a craft—takes time and training. While it is understood that a healthy brain is capable of learning these new skills, how the brain changes in order to develop new behaviors is a relative mystery.
Thursday, July 25, 2019

CMU researchers read data from brains to help people learn

Axios featured work by a group of CMU Engineering researchers connecting with brains to help people learn faster.
Monday, February 18, 2019

Information bottlenecks between brain areas

Byron Yu and ECE postdoc João Semedo found that communication between brain areas occurs through an information bottleneck, which they’ve termed a “communication subspace.”
Wednesday, April 04, 2018

Yu quoted in Quanta on roadblocks to learning

Quanta Magazine quoted Byron Yu and BME’s Steve Chase on their research of learning. Much research on intelligence emphasizes brain plasticity, the ability to respond and adapt to new information.
Tuesday, March 13, 2018

The learning brain is less flexible than we thought

New research from CMU and Pitt reveals that the brain has various mechanisms and constraints by which it reorganizes its neural activity when learning over the course of a few hours. The new research finds that, when learning a new task, the brain is less flexible than previously thought.
Tuesday, August 29, 2017

Yu receives NSF grant for brain research

The National Science Foundation (NSF) recently awarded BME/ECE’s Byron Yu and Professor Matthew Smith from the University of Pittsburgh nearly $500,000 to conduct a research project, titled “Volitional Modulation of neural activity in the visual cortex.” For their project, Yu and Smith will use a brain-computer interface (BCI) to identify which aspects of the brain’s activity are sensory versus cognitive.
Wednesday, May 03, 2017

Yu and Chase receive NIH grant for brain-computer interface research

The goal of the research is to use brain-computer interfaces to study how to accelerate learning.
Tuesday, February 21, 2017

Weldon and Yu receive professorships

Carnegie Mellon University’s Jeffrey Weldon has been awarded The Sathaye Family Foundation Early Career Professorship in Electrical & Computer Engineering, and Byron Yu has been awarded the Gerard G. Elia Career Development Professorship in Engineering. As the highest academic award a university can bestow on a faculty member, professorships are reserved for those who show continued contributions in their field.
Monday, May 09, 2016

BrainHUB announces recipients of ProSEED funding

Carnegie Mellon University has funded four new interdisciplinary neuroscience projects through its ProSEED grant program. The projects aim to create new tools and techniques to vastly improve how scientists study the brain and leverage the university’s strengths in biology, computer science, machine learning, psychology and engineering.
Friday, February 12, 2016

Yu, CMU, and CNBC researchers receive $12m grant

ECE/BME's Byron Yu, BME's Steve Chase, and a team of researchers at the Center for the Neural Basis of Cognition (CNBC) have received a $12 million grant to reverse-engineer the brain in order to make computers think more like humans.