Since January 2018, Yuejie Chi has served as an associate professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, where she holds the Robert E. Doherty Career Development Professorship. She is also an affiliated faculty member in the Machine Learning Department in the School of Computer Science. Previously she was with the Department of Electrical and Computer Engineering and the Department of Biomedical Informatics at The Ohio State University. She received his Ph.D. in Electrical Engineering from Princeton University in 2012, where she was fortunate to work with Robert Calderbank. Chi received an M.A. in Electrical Engineering from Princeton University in 2009, and a B.Eng. in Electronic Engineering from Tsinghua University in 2007.
Chi’s research is motivated by the challenge of efficiently extracting information embedded in a large amount of data, as well as collecting data efficiently to gather actionable information. She is interested in the mathematics of data representation that take advantage of structures and geometry to minimize complexity and improve performance. Specific topics of interest include mathematical and statistical signal processing, machine learning, large-scale optimization, and sampling and information theory, with applications in sensing, imaging and big data.
Chi is a recipient of the NSF CAREER Award, AFOSR Young Investigator Award, ONR Young Investigator Award, ORAU Ralph E. Powe Junior Faculty Enhancement Award, Google Faculty Research Award, IEEE Signal Processing Society Young Author Best Paper Award, and the IEEE ICASSP Best Student Paper Award, among others.
Mathematical Foundations of Data Science
2012 Ph.D., Electrical Engineering, Princeton University
2009 MA, Electrical Engineering, Princeton University
2007 BS, Electronics Engineering, Tsinghua University
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