Dr. Qing Yi is a Computer Scientist at Lawrence Livermore National Laboratory, while holding an adjust associate professor position at Computer Science Department of the University of Colorado, Colorado Springs (UCCS). She was a tenured associate professor of UCCS between 2012-2021 and worked for Google LLC between 2020-2023.  She received her Ph.D. in Computer Science at Rice University under the supervision of Professor Ken Kennedy, where her doctoral dissertation focused on advanced loop optimizations to improve the performance of deep memory hierarchies. She was a post-doctorate associate at the DOE Lawrence Livermore National Laboratory between 2003-2005 and was an assistant professor at the University of Texas at San Antonio between 2005-2012.

Qing Yi's research interests include compilers, programming languages, high performance computing, and software engineering. For over 20 years, her research had focused on developing and specializing automated compiler optimizations to automatically enhance the performance, correctness assurance, and productivity of software development. Before joining LLNL, She has been the lead PI of six multi-year NSF/DOE grants, including the prestigious NSF CAREER award, and has developed the POET transformation language and optimization infrastructure funded both by NSF and by DOE Office of Science (https://github.com/qingyi-yan/POET). While working at Google LLC, she was also responsible for several advanced optimization algorithms inside the XLA compiler for machine learning frameworks.  Her ongoing research interests include the continuous development of new programming language and compiler techniques to enable efficient, correct, and performant software engineering, focusing on supporting program analysis and optimization at the whole-application level and supporting reasoning of the concurrency and computational efficiency of large scientific applications.

Ph.D. Computer Science, Rice University, Houston, TX