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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On March 1, 2023, faculty and undergraduate students from the Faculty of Mathematics participated in what they hope will be merely the first “Math Teach-Off” event.
The premise of the event was simple: three groups of students would take a quiz on the mathematical concept. Then, each group would be taught about the concept for an hour by one of three instructors: Nathaniel Stevens (assistant professor of Statistics and Actuarial Science and Director of the undergraduate Data Science program); Jordan Hamilton (lecturer in the Math Undergraduate Group); and Blake Madill (lecturer in Pure Mathematics). Finally, the students would take a second quiz on the concept, and each group’s scores would be compared for improvement and overall accuracy.
Continue reading on the Faculty of Math's website.
The Department of Statistics and Actuarial Science congratulates professor Nathaniel Stevens as being named the winner of the 2023 ASQ Feigenbaum Medal. This achievement is awarded for the "outstanding characteristics of leadership, professionalism, and potential in the field of quality and also whose work has been or, will become of distinct benefit to mankind." (ASQ)
The citation reads: "For outstanding contributions to the development and application of statistics in business and industry, to the mentoring of undergraduate and graduate students, and to the service of the broad Quality community."
The medal will be presented in Philadelphia at 2023 ASQ's World Conference on Quality and Improvement.
The Department of Statistics and Actuarial Science congratulates professor Stefan Steiner as being named the winner of the 2023 ASQ Shewhart Medal. This achievement is awarded for the "outstanding technical contributions and leadership in the field of modern quality control and improvement, especially through the development of theory, principles, techniques, and successful applications in the area of quality." (ASQ)
The citation reads: "For his outstanding leadership in the field of industrial statistics; for his innovation and technical contributions in measurement system analysis, process monitoring, statistical engineering, and variation reduction; and for his dedicated promotion of quality improvement in business, industry, and healthcare."
The medal will be presented in Philadelphia at 2023 ASQ's World Conference on Quality and Improvement.
Please Note: This seminar will be held in-person.
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Student seminar series Qinglong Tian Location: M3 3127 |
Efficient Label Shift Adaptation through the Lens of Semiparametric Models
Please Note: This seminar will be given in person.
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Statistics and Biostatistics seminar series Lehana Thabane Room: M3 3127 |
Confusions About and Controversies Around Pragmatic Trials
Sponsored by the Faculty of Math Data Science Graduate Programs, please join University of Waterloo expert in artificial intelligence, Professor Pascal Poupart, for a public talk in which he will describe the key technological advances in recent years which were behind, and ultimately facilitated, these breakthroughs.
Coffee and tea will be provided.
Registration is short, sweet and not mandatory. However, it is strongly encouraged and appreciated so we can anticipate the number of participants and provide you with any updates and information closer to the date - thank you!
For more details visit the Data Science website.
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.
