Tiadora Ruza’s journey at the University of Waterloo began long before she started university. The inaugural winner of the Germain-Erdős Award spent many years as a teenager participating in the Math Circles program, a math outreach and enrichment program conducted on campus by the Centre for Education in Math and Computing. Math Circles, Ruza reflects, “allowed me to become familiar with the campus environment and made Waterloo feel like my mathematical home.”
Because of that familiarity, coming to Waterloo to study combinatorics and optimization as well as computer science was a logical choice. “I looked forward to the opportunity to study with other students who were just as passionate about mathematics as I was,” she says. “After arriving here, through orientation, classes, and URAs, I’ve had the opportunity to meet extraordinarily talented and passionate individuals who inspire me in my own mathematical journey.”
Now, as she prepares to graduate this June, Ruza has reached another milestone in her journey: winning the inaugural Germain-Erdős award. The Germain-Erdős Award was established by math graduate David Ash (BMath ’87) to recognize and support undergraduate research. Ash loved the research he did into Combinatorics and Optimization as an undergraduate, and thus established the award to “support the next generation of mathematical researchers.” It is named for two ground-breaking mathematicians — Sophie Germain and Paul Erdős.
Tiadora received the Germain-Erdős award in recognition of the research she has conducted in two fields: Analytic Combinatorics in Several Variables and Hand-Writing Recognition. During her NSERC Undergraduate Student Research Award–sponsored work with Combinatorics and Optimization Professor Stephen Melczer, she sought to prove Local Central Limit Theorems — “in other words, proving that certain objects follow a normal distribution.”
She also did two terms of research with Computer Science Professor George Labahn, considering the problem of mathematics hand-writing recognition — “in particular, how to provide effective recognitions tailored to a certain users’ hand-writing.”