Join us April 17-18, 2023 for two days of research engagement, professional

  • Hear about leading-edge math research and applications from world-class researchers

  • Meet talented graduate and undergraduate students and be inspired by their passion for mathematics and its ability to change the world

  • Connect and share insights with thought leaders, including industry experts, government policy makers, technical experts, and an ever-growing family of successful entrepreneurs with roots in the Faculty of Mathematics.

  • Tour state-of-the-art research labs and take part in research demonstrations

  • Have fun with the math community during socials and more!

Research Themes

Security and Privacy

The Faculty of Mathematics at the University of Waterloo is world-renowned for its research excellence in data science, with faculty members and students exploring foundational and emerging questions in cryptography, privacy, security, machine learning, and the quantum environment. Examples of recent work in this area include:

  • Security and privacy in machine learning: Analyzing and preventing the leakage of private information from machine learning models, enabling provenance verification of machine learning models, analyzing model theft and security-relevant failure modes of machine learning models
  • Traffic classification in an increasingly encrypted web: Service detection, classification, and analysis from encrypted communication using Machine Learning
  • Methods and tools for mapping and optimizing the implementation of quantum circuits to realistic architectures

Michele Mosca

Michele Mosca picture

Security and Privacy Keynote: Michele Mosca

Michele Mosca is co-founder of the Institute for Quantum Computing at the University of Waterloo, a Professor in the Department of Combinatorics & Optimization of the Faculty of Mathematics, and a founding member of Waterloo's Perimeter Institute for Theoretical Physics. He is co-founder and CEO of the quantum-safe cybersecurity company, evolutionQ, and co-founder of the quantum software and applications company, softwareQ. He serves as Chair of the board of Quantum Industry Canada.

He started working in cryptography during his undergraduate studies and obtained his doctorate in Mathematics in 1999 from the University of Oxford on the topic of Quantum Computer Algorithms.  His research interests include algorithms and software for quantum computers, and cryptographic tools designed to be safe against quantum technologies. 

He co-founded the not-for-profit Quantum-Safe Canada, and the ETSI-IQC workshop series in quantum-safe cryptography and is globally recognized for his drive to help academia, industry and government prepare our cyber systems to be safe in an era with quantum computers. 

Dr. Mosca’s awards and honours include 2010 Canada's Top 40 Under 40, Queen Elizabeth II Diamond Jubilee Medal (2013), SJU Fr. Norm Choate Lifetime Achievement Award (2017), and a Knighthood (Cavaliere) in the Order of Merit of the Italian Republic (2018).

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Elham Akbari

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Student Research Spotlight: Elham Akbari

While consensus exists on the importance of encryption to ensure users’ privacy, encrypted traffic poses challenges for traffic classification; an indispensable first step for many network operations carried out by network service providers. A plethora of machine learning models, including deep learners, have been suggested in the literature for encrypted traffic classification, most of which assume access to a labeled training dataset and close training and test data distributions. In this talk, I will present the challenges that an operator will face while working with a model built under those assumptions. I will then present an alternative solution based on few-shot learning. We will see how few-shot learning reduces the number of labeled data samples required for classifying a new dataset but falls short of training a generalizable learner.

Data Integrity and Trust

In a world of ‘Big Data’, integrity and trust are critical aspects of data analytics and management for decision-making. With the data life cycle, data needs to be correct, truthful, complete, valid, and reliable.

  • Leveraging the AI-people partnership: how to enhance and make sense of intelligent systems, addressing transparency, engagement, trust and collaboration
  • Constrained optimization using interior point and trust region approaches analysis of theoretical algorithmic properties and development of efficient and robust software for application in financial services
  • AI-powered meta-analyses: Building systems using AI and neural networks to help users gain insights from large amounts of varied data types

Ihab Ilyas

Ihab Ilyas

Data Integrity and Trust Keynote: Ihab Ilyas

Ihab F. Ilyas is a professor at Cheriton School of Computer Science at the University of Waterloo, and he is NSERC-Thomson Reuters Research Chair on data quality. Professor Ilyas’ main research interest lies in database systems, with focus on probabilistic and uncertain data management, machine learning for data quality and enrichment, and information extraction. He co-founded Tamr Inc. in 2013, a startup focusing on large-scale data integration and data cleaning. He also co-founded Inductiv in 2019 (acquired by Apple), a Waterloo-based startup using AI for structured data cleaning. Professor Ilyas’s research is premised on the belief that poor data quality is a main hurdle to adoption of AI technologies, and machine learning can be leveraged to clean datasets and train effective models accurately and timely at scale, significantly reducing time and labour costs. 

Professor Ilyas is an ACM Fellow and IEEE Fellow, a recipient of the Ontario Early Researcher Award, a Cheriton Faculty Fellowship, an NSERC Discovery Accelerator Award, and a Google Faculty Award. In 2014, Professor Ilyas was a winner of ACM Distinguished Scientist. Additionally, he was an elected member of the VLDB Endowment board of trustees (2016-2021), elected SIGMOD vice chair (2016-2021), an associate editor of the ACM Transactions of Database Systems (2014-2020), and an associate editor of Foundations of Database Systems. Professor Ilyas is currently on leave from the university, leading the Apple knowledge platform at Apple. 

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Aida Sheshbolouki

Research Spotlight Data integrity

Student Research Spotlight: Aida Sheshbolouki

Aida Sheshbolouki is a Ph.D. candidate and a member of the Data Systems Group at the University of Waterloo under the supervision of Prof. M. Tamer Ozsu. Her research involves steps towards data-driven algorithm design for explainable and interpretable analytics over streaming graphs with a particular focus on (2,2)-bicliques (known as butterflies) in bipartite structures.

Health and Medicine

The Faculty of Mathematics is poised to support the University of Waterloo’s long-term planning for Health Futures, as presented in the ‘Waterloo at 100’ vision. The institution aims to be a national and global leader at the interface of health, society, and technology. The Faculty of Mathematics is home to several prominent researchers studying topics at this interface:

  • Mathematical modeling to better understand aspects of health and disease, including cancer treatment, kidney physiology, diabetes, antibiotic resistance, hypertension, epilepsy, hydrocephalus and syringomyelia, as well as modelling of public health and One Health processes
  • Development and application of statistical methods for public health: analysis of life history data, longitudinal data, incomplete data, sequential methods, multivariate analysis, clinical trial design, and the assessment of diagnostic tests
  • Computational health informatics: computational mechanisms for enabling more efficient and easy-to-use healthcare delivery, including large-scale data analytics problems in healthcare
  • Mathematical biology: understanding cellular and biomolecular networks driving gene expression, signal transduction, and metabolism

Anita Layton

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Health and Medicine Keynote: Anita Layton

Anita Layton is the Canada 150 Research Chair in Mathematical Biology and Medicine, and Professor of Applied Mathematics, Computer Science, Pharmacy and Biology at the University of Waterloo. She leads a diverse and interdisciplinary team of researchers who use computational modeling tools to better understand aspects of health and disease. Mathematics is their microscope! The Layton group collaborate with physiologists, biomedical engineers, and clinicians to formulate detailed models of cellular and organ function. Model simulations and predictions are used to answer questions such as: How should anti-hypertensive drugs be prescribed differently for men and women? Is it better to take one's medication in the morning or at night (or does it matter), given the interactions between the drug and our body's circadian rhythms?

Professor Layton is a Fellow of the Association for Women in Mathematics (2022), winner of the Krieger-Nelson Prize (2021), and winner of Canada’s Most Powerful Women: Top 100 Award (2021). She is an Associate Editor of SIAM Review Book Section, an Associate Editor of SIAM Journal on Applied Dynamical Systems, a Section Editor (AI/Machine Learning) of Hypertension, and an Associate Editor of Maple Transactions. Additionally, she serves as the Associate Dean, Research and International, for the Faculty of Mathematics, and chairs the Research Equity, Diversity and Inclusion Council at the University of Waterloo.

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Yue Lyu

Yue Lyu Research spotlight

Student Research Spotlight: Yue Lyu

Yue Lyu is a Master’s student in Human-Computer Interaction at the WatVis Lab in the School of Computer Science, University of Waterloo, under the supervision of Drs. Jian Zhao and Keiko Katsuragawa. Her research focuses on building new user experiences and systems for autistic children by using advanced computer technologies to offer various knowledge and skills presentations as well as create opportunities for children to apply the skills in both the digital and physical worlds. Leveraging her background in computer science and business administration, her work aims to create solutions that are not only innovative but also practical and beneficial to the community. Yue has extensive experience in system design, database design, machine learning engineering, and mobile application development with multiple programming languages and platforms.