Yuejie Chi
Department of Electrical and Computer Engineering
Carnegie Mellon University
Short Bio  /  CV  /  Google Scholar

I am a Professor in the Dept. of Electrical and Computer Engineering at Carnegie Mellon University, where I held the inaugural Robert E. Doherty Career Development Professorship from 2018 to 2020. I am also an affiliated faculty member with the Machine Learning Department and CyLab.

My research interests lie in the theoretical and algorithmic foundations of data science, machine learning, signal processing and inverse problems, with applications in sensing, imaging, decision making, and societal systems, broadly defined. The problems my group studies are often interdisciplinary in nature, lying at the intersection of statistics, learning, optimization, and sensing. Specific lines of research topics can be found here.

I have been lucky to receive a couple of awards for my work, including Presidential Early Career Award for Scientists and Engineers (PECASE) from the White House, the highest honor bestowed by the United States Government to outstanding early-career scientists and engineers who show exceptional promise for leadership in science and technology. In 2019, I received the inaugural IEEE Signal Processing Society Early Career Technical Achievement Award for contributions to high-dimensional structured signal processing. In addition, I received IEEE Signal Processing Society Young Author Best Paper Award, and young investigator awards from several agencies including NSF, ONR and AFOSR.

I am an IEEE Fellow (Class of 2023) for contributions to statistical signal processing with low-dimensional structures. [ECE News] [SCS News]

I also had the privilege to deliver plenary, keynote and tutorial talks at several conferences and workshops, with more materials shared here. I was named the Goldsmith Lecturer by IEEE Information Theory Society in 2021, and a Distinguished Lecturer by IEEE Signal Processing Society for 2022-2023.

Previously I was with the Dept. of Electrical and Computer Engineering and the Dept. of Biomedical Informatics at The Ohio State University until 2017. I completed my Ph.D. in Electrical Engineering from Princeton University in 2012, where I was fortunate to be advised by Prof. Robert Calderbank. I received a M.A. in Electrical Engineering from Princeton University in 2009, and a B.Eng. in Electronic Engineering from Tsinghua University in 2007.

I have an Erdos number of 3.

Notes to prospective students outside CMU: There is on expectation 1 slot in my group every year. Unfortunately, I am not able to respond to most (nearly all!) inquiries about suitability in my group due to the volume of emails I receive. Instead, please apply directly to the ECE department and list me as a faculty of interest. Occasionally, I take summer interns and visitors with strong mathematical backgrounds.

Notes to students at CMU: Please contact me directly if you're interested in research projects in my group.

Teaching in Spring 2023

ECE 18-813B: Special Topics in Artificial Intelligence: Foundations of Reinforcement Learning

Upcoming Events

  • Aug. 2023: Short course on "Statistical and Algorithmic Foundations of Reinforcement Learning" at JSM 2023, joint with Y. Wei and Y. Chen at Wharton.

  • Jun. 2023: Tutorial on "Advances in Federated Optimization: Efficiency, Resiliency, and Privacy" at ICASSP 2023, joint with Zhize Li.

  • May 2023: Plenary speaker at the inaugural CAMDA Conference at TAMU.

  • Mar. 2023: Plenary speaker at IEEE Annual Computing and Communication Workshop and Conference (CCWC), virtual.

Recent Papers


  • Office: Porter Hall B25

  • Mailing address: 5000 Forbes Ave., Pittsburgh, PA 15213

  • Email: first+last at cmu dot edu = first+c at andrew dot cmu dot edu