Dr. Yuejie Chi

Based in Pittsburgh, PA, United States

Bio:

Dr. Yuejie Chi is the Sense of Wonder Group Endowed Professor of Electrical and Computer Engineering in AI Systems at Carnegie Mellon University, with courtesy appointments in the Machine Learning department and CyLab. She received her Ph.D. and M.A. from Princeton University, and B. Eng. (Hon.) from Tsinghua University, all in Electrical Engineering. Her research interests lie in the theoretical and algorithmic foundations of data science, signal processing, machine learning and inverse problems, with applications in sensing, imaging, decision making, and societal systems, broadly defined. Among others, Dr. Chi received the Presidential Early Career Award for Scientists and Engineers (PECASE), the inaugural IEEE Signal Processing Society Early Career Technical Achievement Award for contributions to high-dimensional structured signal processing, and IEEE Signal Processing Society Young Author Best Paper Award. She was named a Goldsmith Lecturer by IEEE Information Theory Society, and a Distinguished Lecturer by IEEE Signal Processing Society. She currently serves or served as an Associate Editor for IEEE Trans. on Information Theory, IEEE Trans. on Signal Processing, IEEE Trans. on Pattern Recognition and Machine Intelligence, Information and Inference: A Journal of the IMA, and SIAM Journal on Mathematics of Data Science. She is an IEEE Fellow (Class of 2023) for contributions to statistical signal processing with low-dimensional structures.

Available Lectures

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A Tale of Preconditioning and Overparameterization in Ill-conditioned Low-rank Estimation
Many problems encountered in science and engineering can be formulated as estimating a low-rank object from incomplete, and possibly corrupted, linear measurements. Through the lens of matrix and...
From Single-agent to Federated Reinforcement Learning
Reinforcement learning (RL) is garnering significant interest in recent years due to its success in a wide variety of modern applications. Q-learning, which seeks to learn the optimal Q-function...
Provable Learning from Data with Priors: from Low-rank to Diffusion Models
Generative priors are effective tools to combat the curse of dimensionality, and enable efficient learning that otherwise will be ill-posed, in data science. This talk starts with the classical...
Statistical Foundations of Reinforcement Learning Through a Non-asymptotic Lens
Reinforcement learning (RL) is garnering significant interest in recent years due to its success in a wide variety of modern applications. However, theoretical understandings on the...

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