Yuejie Chi, PhD

Yuejie Chi's research interests lie in the theoretical and algorithmic foundations of data science, generative AI, reinforcement learning, and signal processing, motivated by applications in scientific and engineering domains, including neuroscience. She is interested in improving the performance, efficiency, and reliability of reasoning and decision-making in artificial and natural intelligent systems, driven by data-intensive but resource-constrained scenarios.

deep learning ;  generative models ;  neural networks ;  optimization ;  signal processing ;  statistical methods ;  superresolution imaging  

algorithms ;  artificial intelligence ;  machine learning  

Chi is the Charles C. and Dorothea S. Dilley Professor of Statistics and Data Science at Yale University, with a secondary appointment in Computer Science. Among others, Chi received the Presidential Early Career Award for Scientists and Engineers (PECASE), the SIAM Activity Group on Imaging Science Best Paper Prize, the IEEE Signal Processing Society Young Author Best Paper Award, and the inaugural IEEE Signal Processing Society Early Career Technical Achievement Award for contributions to high-dimensional structured signal processing. She is an IEEE Fellow (Class of 2023) for contributions to statistical signal processing with low-dimensional structures.