Yixin Chen

Yixin Chen is a professor of computer science and engineering at Washington University in St. Louis. He is known for his contributions to deep learning systems.

Chen is an IEEE Fellow and an AAAI Fellow.

Life and career Chen completed his Bachelor's in Computer Science from the University of Science and Technology of China in 1999 and Master's in Computer Science from the University of Illinois at Urbana-Champaign in 2001. He then pursued his Ph.D. in computer science from the University of Illinois at Urbana-Champaign and completed it in 2005.

Chen also developed a compression framework for convolutional neural networks (CNNs). His lab invented a frequency-sensitive compression technique in which more important model parameters are better preserved.

Deep learning on graphs and time series Chen and his students proposed DGCNN, one of the first graph convolution techniques that can learn a meaningful tensor representation from arbitrary graphs, and showed its deep connection to the Weisfeiler-Lehman algorithm. They applied GNNs to link prediction (in the well-known SEAL algorithm) and matrix completion and achieved record results.

For time series classification, Chen advocated using a multi-scale convolutional neuronal network, also known as MCNN, citing its computational efficiency. He illustrated that MCNN brings out features at varying frequencies and scales by leveraging GPU computing, contrary to other frameworks that can only retract features at a single-time-scale.

Awards and honors 2007 – Microsoft Research New Faculty Fellowship 2022 – Fellow, Institute of Electrical and Electronics Engineers 2025 – Fellow, Association for the Advancement of Artificial Intelligence