Dr. Yiyu Shi

Based in Notre Dame, IN, United States

Bio:

Dr. Yiyu Shi is currently a professor in the Department of Computer Science and Engineering at the University of Notre Dame, the site director of National Science Foundation I/UCRC Alternative and Sustainable Intelligent Computing, and the director of the Sustainable Computing Lab (SCL). He is also a visiting scientist at Boston Children’s Hospital, the primary pediatric program of Harvard Medical School. He received his B.S. in Electronic Engineering from Tsinghua University, Beijing, China in 2005, the M.S and Ph.D. degree in Electrical Engineering from the University of California, Los Angeles in 2007 and 2009 respectively. His current research interests focus on hardware intelligence and biomedical applications. In recognition of his research, more than a dozen of his papers have been nominated for or awarded as the best paper in top journals and conferences, including the 2021 IEEE Transactions on Computer-Aided Design Donald O Pederson Best Paper Award. He is also the recipient of Facebook Research Award, IBM Invention Achievement Award, Japan Society for the Promotion of Science (JSPS) Faculty Invitation Fellowship, Humboldt Research Fellowship, IEEE St. Louis Section Outstanding Educator Award, Academy of Science (St. Louis) Innovation Award, Missouri S&T Faculty Excellence Award, NSF CAREER Award, IEEE Region 5 Outstanding Individual Achievement Award, the Air Force Summer Faculty Fellowship, and IEEE Computer Society Mid-Career Research Achievement Award. He has served on the technical program committee of many international conferences. He is the deputy editor-in-chief of IEEE VLSI CAS Newsletter, and an associate editor of various IEEE and ACM journals. He was the education chair of ACM SIGDA (2018-2021). He is an IEEE CEDA distinguished lecturer and an ACM distinguished speaker.

Available Lectures

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Hardware-aware Machine Learning for Biomedical Applications
With the prevalence of deep neural networks, machine intelligence has recently demonstrated performance comparable with, and in some cases superior to, that of human experts in medical imaging and...
Hardware/Software Co-Design Towards Quantum Advantages
Despite the pursuit of quantum advantages in various applications, the power of quantum computers in executing neural network has mostly remained unknown, primarily due to a missing tool that...
Hardware/Software Co-Design Towards TinyML
In the past a few years, powered by the strong need of edge intelligence, there has been an increasing interest in deploying deep neural networks on tiny hardware with limited computing power and...

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