The Department of Statistics and Actuarial Science is a top-tier academic unit among statistical and actuarial science globally. Our community is engaged in topics such as actuarial science, biostatistics, data science, quantitative finance, statistics, & statistics-computing. Our department is home to more than 60 full-time faculty researching diverse and exciting areas, over 1000 undergraduate students from around the world, and more than 175 graduate students in master, doctoral, and professional programs.
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The Department of Statistics and Actuarial Science would like to congratulate Zhanyi Jiao, Benxuan Shi, and Yuanyuan Zhang for being three of the six new recipients of the Society of Actuaries (SOA) Hickman Scholarship for 2023-2024, and to Yiping Guo, Liyuan Li, and Michael Zhu for having their stipend renewed.
The Hickman Scholars Program provides highly prestigious awards for PhD students in North America who work in fields related to Actuarial Science. The program provides stipends to doctoral candidates who will, through their studies and careers, address the research and education needs of the actuarial profession.
DataFest Teams from left: Teams "Food," "Data Divas," and "DataCraft"
From April 28-30, the Department of Statistics and Actuarial Science hosted the American Statistical Association (ASA) DataFest: an international data challenge in which teams of undergraduates work to “find and share meaning in a large, rich, and complex data set.” This year, 75 students participated in 20 teams, with 15 teams completing Waterloo’s intense 48-hour challenge. While teams were limited to undergraduates, they had the chance throughout to interact with graduate students, professors, and representatives of Scotiabank, the Waterloo challenge’s major sponsor.
"A key highlight from this competition was getting the opportunity to network with industry professionals, mentors, and professors,” says Inaara Somani. “It was so valuable and insightful to converse with them about their personal knowledge and experience, as well as pitch our ideas…Not only were these individuals great help during the competition, but it was so exciting to learn new things from them!"
Continue reading the full story on the Faculty of Mathematics website.
Diana Skrzydlo is one of four recipients of this year’s Distinguished Teacher Award from the Centre for Teaching Excellence.
Skrzydlo is the Director of the MActSc program, a Continuing Lecturer for the department of Statistics and Actuarial Science, and the Math Teaching Fellow.
Read the full story on the Faculty of Mathematics website.
Please Note: This seminar will be given in person.
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Statistics and Biostatistics seminar series Qingrun Zhang Room: M3 3127 |
Stabilized Core gene and Pathway Election uncovers pan-cancer shared pathways and a cancer-specific driver
Please Note: This seminar will be given in-person.
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Distinguished Lecture Series Vladimir Vovk Room: DC 1302 |
Nonparametric prediction and testing
The assumption of randomness (the data are generated in the IID fashion) is the key assumption used in machine learning and much of nonparametric statistics. My main topics will be prediction under this assumption and testing the assumption. The treatment of both topics will be based on the relatively recent technique of conformal prediction, but I will try to connect it with David A. Sprott's ideas and areas of research pointing out some paradoxical features of prediction and testing in the framework of unrestricted randomness. On one hand, they are barely possible, in the sense that the existence of non-trivial prediction and testing procedures is fragile: they cease to exist when the problem settings are modified in natural ways. On the other, such procedures can be highly efficient in realistic situations; I will give both theoretical and experimental results demonstrating this.
Please Note: This seminar will be given in person.
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Statistics and Biostatistics seminar series Elena Tuzhilina Room: M3 3127 |
TBA
Contact Information:
Christiane Lemieux
Christiane Lemieux's personal website
Research interests
Professor Lemieux is interested in quasi-Monte Carlo methods and their applications. These methods can be thought of as deterministic versions of the well-known and highly used Monte Carlo method. They are designed to improve upon the performance of Monte Carlo by replacing random sampling by a more uniform sampling mechanism based on low-discrepancy point sets. A major goal of Professor Lemieux's research is to improve the applicability of quasi-Monte Carlo methods to a wide variety of practical problems.
