David R. Cheriton School of Computer Science | Cheriton School of Computer Science | University of Waterloo
- ️Thu May 01 2025
The Cheriton School of Computer Science is named for David R. Cheriton, who earned his PhD in Computer Science at the University of Waterloo in 1978. In 2005, Professor Cheriton made a transformational gift to the school that supports named chairs, faculty fellowships, and graduate scholarships.
Discover our latest achievements by following our news. Upcoming talks on a range of computer science topics are found under events.
Please go to contact, open positions or visit if you have a question about school programs or services, would like to know more about faculty positions available or plan to visit our school.
News
Professor Xiao Hu, and her collaborators have received a Distinguished Paper Award at the 2025 ACM SIGMOD/PODS International Conference on Management of Data.
Their paper, Fast Matrix Multiplication Meets the Submodular Width, introduces a new and unified framework for determining how efficiently any Boolean conjunctive query can be answered using fast matrix multiplication techniques.
Instead of typing furiously and constantly hitting backspace, what if you could code by just drawing out your ideas?
This vision is becoming a reality thanks to Ryan Yen (MMath ’24), a recent master’s graduate of the Cheriton School of Computer Science, and Professors Jian Zhao and Daniel Vogel. While at Waterloo, Yen co-developed Code Shaping, an AI-powered software that allows programmers to edit their code through free-form sketches.
Professor Freda Shi was featured in Toyota Technological Institute at Chicago's (TTIC), alumni highlight series.
Freda Shi, Ph.D. graduate from TTIC’s class of 2024 (advised by Professors Karen Livescu and Kevin Gimpel), joined the Cheriton School of Computer Science at the University of Waterloo as a tenure-track Assistant Professor in July 2024. In September 2024 she was named a CIFAR AI Chair and a faculty member at the Vector Institute.
Freda’s research focuses on computational linguistics and natural language processing, aiming to deepen the understanding of both natural language and human language processing. She explores how these insights can enhance the design of more efficient, effective, safe, and trustworthy NLP systems. She is particularly interested in learning language through grounding, computational multilingualism, and related machine learning aspects.