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Anudeep Das, Vasisht Duddu, Rui Zhang and N. Asokan have received the Best Paper Award at CODASPY 2025, the 15th ACM Conference on Data and Application Security and Privacy.

Their paper, Espresso: Robust Concept Filtering in Text-to-Image Models, introduces a new technique to improve the effectiveness, safety and reliability of generative AI systems that create images from natural language text prompts.

Professor Jian Zhao has received an Ontario Early Researcher Award, which will provide $140,000 in funding to support his research on enhancing software development through visual interfaces and generative AI. 

The funding from the Ontario government is matched by an additional $50,000 from the University of Waterloo, bringing total funding to $190,000 over five years.

Professor Gautam Kamath has been awarded $140,000 from the Ontario Early Researcher Awards program to further his research on algorithms and machine learning techniques that preserve data privacy. 

The amount from the Ontario government is matched by $50,000 from the University of Waterloo, bringing total funding to $190,000.

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.

Professor Xiao Hu has received a Best Paper Award at the 2025 ACM SIGMOD/PODS International Conference on Management of Data for her research on optimizing join-aggregate queries.

Her paper, Output-Optimal Algorithms for Join-Aggregate Queries, addresses a long-standing open problem in database theory, establishing output-optimal bounds on the efficiency with which such queries can be processed.