Welcome to the David R. Cheriton School of Computer Science
The Cheriton School of Computer Science is named for David R. Cheriton, who earned his PhD in Computer Science in 1978, and made a transformational gift to the school in 2005. It has become the largest academic concentration of Computer Science researchers in Canada.
- Jan. 17, 2019
Organizations looking to benefit from the artificial intelligence (AI) revolution should be cautious about putting all their eggs in one basket, a study from the University of Waterloo has found.
The study, published in Nature Machine Intelligence, found that contrary to conventional wisdom, there can be no exact method for deciding whether a given problem may be successfully solved by machine learning tools.
- Jan. 11, 2019
Professor Shai Ben-David and his colleagues Pavel Hrubes, Shay Moran, Amir Shpilka and Amir Yehudayoff have shown that a simple machine learning problem — whether an algorithm can extract a pattern from limited data — is mathematically unsolvable
- Jan. 10, 2019
The following article, titled “Women Attorneys in Tech: Four Industry Leaders Talk About Their Work,” originally appeared in the January/February 2019 issue of New York State Bar Association Journal. Grossman, a Research Professor in the Cheriton School of Computer Science, was recently appointed as Director of Women in Computer Science.
The article, by Mark A. Berman, Editor, New York State Bar Association Journal, showcases four exceptional women attorneys in tech — Shoshanah Bewlay, Gail Gottehrer, Sandra Rampersaud and Maura Grossman.
- Jan. 22, 2019
David Lepofsky, LLB, Osgoode Hall Law School, LL.M, Harvard Law School
Chair, Accessibility for Ontarians with Disabilities Act Alliance Adjunct Professor, Osgoode Hall Law School
- Jan. 24, 2019
Eitan Grinspun, Associate Professor of Computer Science and Applied Mathematics
- Jan. 24, 2019
Brandon Alcox, Master’s candidate
David R. Cheriton School of Computer Science
This thesis investigates the application of various fields of artificial intelligence to the domain of sports management and analysis. The research in this thesis is primarily focused on the entry draft for the National Hockey League, though many of the models proposed may be applied to other sports and leagues with minimal adjustments.