Department of Mathematics and Statistics

Outstanding 4th Year Engineering Students Honoured

Wednesday, April 7th, 2021

On March 31, 2021, the Faculty of Engineering and Applied Science recognized and celebrated 4th year engineering students showing outstanding achievement in academics. The annual reception honoured students who maintained a 3.5+ GPA across all four years of study.

Department Colloquium - Qiyang Han (Rutgers University)

Friday, March 26th, 2021

Time: 2:30 p.m. Place: Online (via Zoom)

Speaker: Qiyang Han (Rutgers University)

Title: Multiple isotonic regression: limit distribution theory and confidence intervals.

Abstract: In the first part of the talk, we study limit distributions for the tuning-free max-min block estimators in multiple isotonic regression under both fixed lattice design and random design settings. We show that at a fixed interior point in the design space, the estimation error of the max-min block estimator converges in distribution to a non-Gaussian limit at certain rate depending on the number of vanishing derivatives and certain effective dimension and sample size that drive the asymptotic theory. The limiting distribution can be viewed as a generalization of the well-known Chernoff distribution in univariate problems. The convergence rate is optimal in a local asymptotic minimax sense. In the second part of the talk, we demonstrate how to use this limiting distribution to construct tuning-free pointwise nonparametric confidence intervals in this model, despite the existence of an infinite-dimensional nuisance parameter in the limit distribution that involves multiple unknown partial derivatives of the true regression function. We show that this difficult nuisance parameter can be effectively eliminated by taking advantage of information beyond point estimates in the block max-min and min-max estimators through random weighting. Notably, the construction of the confidence intervals, even new in the univariate setting, requires no more efforts than performing an isotonic regression for once using the block max-min and min-max estimators, and can be easily adapted to other common monotone models. This talk is based on joint work with Hang Deng and Cun-Hui Zhang.

Qiyang Han is an Assistant Professor of Statistics at Rutgers University. He received his Ph.D. in Statistics from University of Washington in 2018. He is broadly interested in mathematical statistics and high dimensional probability. His current research is concentrated on abstract empirical process theory and its applications to nonparametric function estimation, Bayes nonparametrics, and high dimensional statistics.

Tags:

Queen’s University Scientista - 4th annual conference

Sunday, March 21st, 2021

Queen’s University Scientista is thrilled to present our 4th annual conference, Empower! We invite you to join us on Sunday, March 21st, 2021, from 11am-4pm for an invigorating day consisting of a discussion panel, keynote speeches, and interactive workshops featuring speakers across different sectors of Science, Technology, Engineering, Mathematics, and Medicine. We aspire to provide engaging and thought-provoking conversations to ensure self-identifying women, gender non-conforming individuals, and allies in STEMM feel confident and empowered on their paths to success.

Tags:

Department Colloquium - Mike Hill (UCLA)

Friday, March 19th, 2021

Time: 2:30 p.m. Place: Online (via Zoom)

Speaker: Mike Hill (UCLA)

Title: Counting exotic spheres.

Abstract: The circle, surfaces, and three manifolds have essentially one smooth structure on them: there is a unique way to "do calculus" on these. For dimensions at least 5, ordinary Euclidean space does too. In 1956, Milnor shocked the mathematical community by showing that this is not the case for spheres: the 7-sphere has "exotic" smooth structures! In this talk, I will discuss the question "how many distinct smooth structures are there on a given sphere?'' In particular, I will describe some work addressing when the only smooth structure on a sphere is the usual one.

Mike Hill is a Professor at the University of California, Los Angeles. His research focus is in algebraic topology. Prof. Hill completed his Ph.D. at the Massachusetts Institute of Technology in 2006. Prior to joining UCLA in 2015, he was a faculty member at the University of Virginia. He is an editor for Mathematische Zeitschrift, Documenta Mathematica and the Transactions of the American Mathematical Society.

Tags:

Department Colloquium - Boris Hasselblatt (Tufts University)

Friday, March 12th, 2021

Time: 2:30 p.m. Place: Online (via Zoom)

Speaker: Boris Hasselblatt (Tufts University)

Title: Symmetries of dynamical systems.

Abstract: Examples of dynamical systems with symmetries are not particularly rare, but it is often clear that perturbations can break such symmetry. Thus, it is natural to expect that the presence of symmetries is exceptional. We describe some results about continuous, discrete, and trivial symmetry groups of flows (continuous-time dynamical systems) in the categories of smooth flows and of continuous flows.

Boris Hasselblatt is a professor of mathematics as well as former department chair and Associate Provost at Tufts University. He obtained his PhD from Caltech, before moving to Tufts in 1989. His research centers on smooth, geometrically motivated, and topological dynamical systems with hyperbolic behavior. He has coauthored the book Introduction to the Modern Theory of Dynamical Systems, which is the reference in dynamics, and one of the top 100 most cited books in mathematics. He is a Fellow of the American Mathematical Society, held the Chaire Jean Morlet at CIRM in Marseille, as well as many visiting positions, including at the ETH Z\"{u}rich, the University of Tokyo and the IHES. He cofounded the Journal of Modern Dynamics, Electronic Research Announcements - Mathematical Sciences, and Mathematics Research Reports, and he currently serves as the eleventh Secretary of the American Mathematical Society.

Tags:

Department Colloquium - Caroline Colijn (Simon Fraser)

Friday, March 5th, 2021

Time: 2:30 p.m. Place: Online (via Zoom)

Speaker: Caroline Colijn (Simon Fraser University)

Title: Mathematical models and the case for vaccinating essential workers sooner

Abstract: We develop the first mathematical model incorporating long COVID and COVID complications. It also includes an age- and essential-work contact structure. We compare the model to BC data on incident COVID-19 cases, hospitalizations and deaths. With good agreement we proceed to compare the benefits of different vaccine rollout strategies. We find that where the pandemic is controlled with widespread distancing, limiting the transmission rate, strategies that prioritize high-contact workers early in the program have considerable benefits compared to oldest-first orders. Benefits hold for infections, hospitalizations and deaths, as well as for long COVID and chronic impacts. We estimate large net monetary benefits saved by such a program. Finally, we illustrate the intuition behind this result using a simple model accounting for a higher risk of a severe outcome in some groups (eg the elderly) vs a higher risk of exposure and transmission in others (eg high-contact workers).

Caroline Colijn joined Simon Fraser University's Department of Mathematics in 2018 as a Canada 150 Research Chair in Mathematics for Evolution, Infection and Public Health. Her work is at the interface of mathematics and the epidemiology and evolution of pathogens. Prof. Colijn was recently awarded the Radio-Canada Scientist of the Year for 2020.

Tags:

Department Colloquium - Idris Assani (University of North Carolina)

Friday, February 26th, 2021

Time: 2:30 p.m. Place: Online (via Zoom)

Speaker: Idris Assani (University of North Carolina)

Title: Recurrence and Return Times

Abstract: We present some pointwise results on the topic of the pointwise convergence of multiple ergodic averages. These include our work on the averages along the cubes, on the Furstenberg averages and on the return times. We also raise some open questions.

Idris Assani is a Professor in the Department of Mathematics of at the University of North Carolina at Chapel Hill. His research area is in ergodic theory. He is the author of the research monograph Wiener Wintner Ergodic Theorems about mathematics related to the Wiener–Wintner theorem, and is also the editor of many volumes of collected papers in ergodic theory. Prof. Assani was named as one of the inaugural fellows of the American Mathematical Society in 2012 and one of the 66 most influential mathematicians throughout its history by The Royal Society on World Maths Day in 2017.

Tags:

Queen's Math & Stats Professors receive best SICON Paper Prize

Left to right: Bahman Gharesifard, Abdol-Reza Mansouri

February 19th, 2021

Congratulations to Bahman Gharesifard, Abdol Reza Mansouri, and Drew Steeves (a former MTHE and MSc student) on being awarded SIAM Activity Group on Control and Systems Theory Best SICON Paper Prize.

Tags:

Department Colloquium - Yihong Wu (Yale University)

Friday, February 12th, 2021

Time: 2:30 p.m. Place: Online (via Zoom)

Speaker: Yihong Wu (Yale University)

Title: Self-regularizing Property of Nonparametric Maximum Likelihood Estimator in Mixture Models

Abstract: Introduced by Kiefer and Wolfowitz 1956, the nonparametric maximum likelihood estimator (NPMLE) is a widely used methodology for learning mixture models and empirical Bayes estimation. Sidestepping the non-convexity in mixture likelihood, the NPMLE estimates the mixing distribution by maximizing the total likelihood over the space of probability measures, which can be viewed as an extreme form of overparameterization. In this work we discover a surprising property of the NPMLE solution. Consider, for example, a Gaussian mixture model on the real line with a subgaussian mixing distribution. Leveraging complex-analytic techniques, we show that with high probability the NPMLE based on a sample of size n has $O(\log n)$ atoms (mass points), significantly improving the deterministic upper bound of n due to Lindsay 1983. Notably, any such Gaussian mixture is statistically indistinguishable from a finite one with $O(\log n)$ components (and this is tight for certain mixtures). Thus, absent any explicit form of model selection, NPMLE automatically chooses the right model complexity, a property we term self-regularization. Statistical applications and extensions to other exponential families will be given. Time permitting, we will discuss some recent results on optimal regret in empirical Bayes and the role of NPMLE. This is based on joint work with Yury Polyanskiy (MIT).

Yihong Wu is an Associate Professor in the Department of Statistics and Data Science at Yale University. He obtained his Ph.D.~in Electrical Engineering from Princeton University in 2011. He was a Postdoctoral Fellow at the University of Pennsylvania (2011 - 2012), and an Assistant Professor at the University of Illinois at Urbana-Champaign (2013 - 2016). He is broadly interested in theoretical and algorithmic aspects of high-dimensional statistics, information theory, and optimization. He has received many awards, including the Sloan Research Fellowship in Mathematics in 2018, the NSF CAREER award in 2017, the Simons-Berkeley Research Fellowship in 2015, and the Marconi Society Paul Baran Young Scholar Award in 2011.

Tags: