David R. Cheriton Faculty Fellowships in Computer Science

The David R. Cheriton Faculty Fellowships are a prestigious recognition. The awards support the work of leading faculty in computer science with an emphasis on supporting research that addresses problems associated with designing and implementing efficient and reliable computing systems, along with their effective integration.

These fellowships help the University of Waterloo and the David R. Cheriton School of Computer Science continue their innovations in information technology teaching and research.

Current Cheriton Faculty Fellows

2022–2025

Eric Blais

Eric Blais is an Associate Professor in the Algorithms and Complexity Theory group within the Cheriton School of Computer Science. His research interests span theoretical computer science with special focus on sublinear-time algorithms, randomness in computation, and complexity theory. 

His recent achievements include being the recipient of a prestigious best paper award at FOCS 2020 for work with his colleague Professor Shalev Ben-David that extended Yao’s minimax theorem. In 2021, Professor Blais received an Ontario Early Researcher Award to develop new theoretical foundations for sublinear-time algorithms.

He has a PhD from Carnegie Mellon University and was a Simons Foundation Postdoctoral Fellow at MIT from 2012–14.

Semih Salihoğlu

Semih Salihoğlu is an Assistant Professor at the Cheriton School of Computer Science. His research focuses on graph databases, distributed systems for processing graphs, and algorithms and theories for evaluation of database queries. His systems work focuses on developing systems for managing, querying, or doing analytics on graph-structured data. He has co-architected and co-developed several graph data management, processing, visualizing, and debugging systems.

Some of his most notable systems include the GraphflowDB graph database management system and the GPS graph processing system. He has served as the PC co-chair for SIGMOD’s demonstration track and co-chaired the GRADES-NDA workshop, the premier workshop on graph data management, and serves on its steering committee. He has a PhD from Stanford and is a recipient of the 2018 VLDB best paper award.

2021–2024

Jesse Hoey

Jesse Hoey is a Professor at the Cheriton School of Computer Science, where he leads CHIL — the Computational Health Informatics Laboratory. He is a Faculty Affiliate at the Vector Institute and an affiliate scientist at the Toronto Rehabilitation Institute. 

Dr. Hoey holds a PhD in computer science from the University of British Columbia and has published over one hundred peer-reviewed papers. 

His primary research interest is to understand the nature of human emotional intelligence by attempting to build computational models of some of its core functions, and to apply them in domains with social and economic impact. He is an associate editor for the IEEE Transactions on Affective Computing and an Area Chair for the International Joint Conferences on Artificial Intelligence (IJCAI 2021).

M. Tamer Özsu

M. Tamer Özsu is a University Professor at the Cheriton School of Computer Science. Previously, he was the Director of the Cheriton School of Computer Science and the Associate Dean (Research) of the Faculty of Mathematics. His research is on distributed data management and the management of non-conventional data. He is a Fellow of the Royal Society of Canada, American Association for the Advancement of Science, Association for Computing Machinery and Institute of Electrical and Electronics Engineers, an elected member of Science Academy, Turkey and a member of Sigma Xi. He is the Founding Editor-in-Chief of ACM Books (2014–20) and Synthesis Lectures on Data Management (2009–14). University Professor Özsu is the recipient of the ACM SIGMOD Test-of-Time Award, the ACM SIGMOD Contributions Award, and The Ohio State University College of Engineering Distinguished Alumnus Award. 

2020–2023

Christopher Batty

Christopher Batty is an Associate Professor in the Cheriton School of Computer Science. Professor Batty received his PhD from the University of British Columbia in 2010 and was a Banting Postdoctoral Fellow at Columbia University from 2011 to 2013. He has also spent time with two leading visual effects studios: Frantic Films (Canada) and Weta Digital (New Zealand).

Professor Batty’s research focuses on physical simulation techniques for applications in computer graphics and computational physics. A major thrust of his work considers the development of efficient numerical and geometric methods to represent and animate the diverse behaviors of liquids, including viscous and non-Newtonian flows, interactions between fluids and solids (hair, cloth, rigid bodies, etc.), surface tension effects, bubbles and foams, and more.

Elements of his research have been incorporated into commercial software for the visual effects industry, including Side Effects Software’s Houdini and Autodesk’s Maya/Bifrost, and used in numerous blockbuster movies.

Yaoliang Yu

Yaoliang Yu is an Assistant Professor in the Cheriton School of Computer Science, a member of the AI group and the Waterloo AI Institute, and a Canada CIFAR AI chair at the Vector Institute. He obtained his PhD from the computing science department at University of Alberta, winning an Outstanding Thesis Award, and a PhD Dissertation Award from the Canadian Artificial Intelligence Association. He spent two postdoctoral years at Carnegie Mellon University before joining University of Waterloo. He has served in the program committee of major AI/ML conferences and received best reviewer awards from NeurIPS and ICML.

His main research area is in machine learning and optimization. Topics closest to his heart include (a) design and analysis of efficient, scalable and distributed gradient-type algorithms for convex and nonconvex optimization; (b) understanding the robustness of machine learning models under random and adversarial perturbations; (c) analysis and application of kernel methods; and lately (d) deep generative models where he is fascinated by a multitude of ways to push probability densities back and forth, using ideas from probability theory, convex analysis, and deep learning. Constantly pushed by his students, he has always enjoyed learning more about vision and natural language challenges.

Previous Cheriton Faculty Fellows