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Wednesday, March 19, 2025

Congratulations to Alexandra Bühler, Graduate Research Excellence Awards recipient

Alexandra Bühler co-won a Graduate Research Excellence Award ($2500) for her paper “Multistate models as a framework for estimand specification in clinical trials of complex processes.” Alexandra graduated in 2024 with a PhD in Biostatistics.

Alexandra's co-supervisors Richard Cook and Jerry Lawless remarked that "Alexandra was an exceptional doctoral student and a joy to supervise. It is very gratifying to see her creative and important work recognized through this award. Notably, the same paper was recently recognized as one of the top 10 most cited papers in Statistics in Medicine in 2023." 

Congratulations to Sean Monahan (Pure Math) and Sepehr Hajebi (Combinatorics and Optimization) who also received a Graduate Research Excellence Award for their work. Learn more by reading the full award announcement.

Chris Salahub
Thursday, April 15, 2021

The story behind the data

As a master’s student, Chris Salahub completed a project focused on reducing the risk for cyclists on the busy streets of Zurich. His passion for leveraging data to solve real-world problems is what drew him back to the Faculty of Mathematics, where he previously earned a bachelor’s degree, to pursue a PhD in statistics.

Jennifer Haid
Thursday, March 4, 2021

The power of curiosity

“I like to think I’m a curious person,” said Jennifer Haid (BMath’04), a native of the Waterloo Region. When she learned about Waterloo’s Math Day from her high school math teacher, she decided to attend in hopes of learning about a career path that would leverage her aptitude for mathematics in the business world. “I remember watching a professor deliver a presentation about actuarial science and thinking two things: It was challenging, and I could do it,” she shared. 

Katia Naccarato
Thursday, February 11, 2021

The Right Direction

Katia Naccarato has never shied away from exploring an unfamiliar path, hitting a dead end, and trying a different one. Before she enrolled in the Master of Actuarial Science (MActSc) program at the Faculty of Mathematics, she was laser-focused on pursuing a career in medicine. Her current trajectory looks nothing like she expected, but she’s confident she’s heading in the right direction.

Samantha Wallis
Thursday, February 4, 2021

Laying a foundation

Samantha Wallis’s enthusiasm for statistics is matched only by her longtime passion for visual arts. As she considers her path forward after graduating with a degree in mathematics, Wallis thinks about how to meld her two interests into a single career. While she hasn’t landed on a definitive answer, she has a strong hunch where she will go next.

Thursday, September 10, 2020 4:00 pm - 4:00 pm EDT (GMT -04:00)

Department seminar by Emma Jingfei Zhang, Miami University

Network Response Regression for Modeling Population of Networks with Covariates

Multiple-network data are fast emerging in recent years, where a separate network over a common set of nodes is measured for each individual subject, along with rich subject covariates information. Existing network analysis methods have primarily focused on modeling a single network, and are not directly applicable to multiple networks with subject covariates.

In this talk, we present a new network response regression model, where the observed networks are treated as matrix-valued responses, and the individual covariates as predictors. The new model characterizes the population-level connectivity pattern through a low-rank intercept matrix, and the parsimonious effects of subject covariates on the network through a sparse slope tensor. We formulate the parameter estimation as a non-convex optimization problem, and develop an efficient alternating gradient descent algorithm. We establish the non-asymptotic error bound for the actual estimator from our optimization algorithm. Built upon this error bound, we derive the strong consistency for network community recovery, as well as the edge selection consistency. We demonstrate the efficacy of our method through intensive simulations and two brain connectivity studies.

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Meeting ID: 844 283 6948
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