Awarded annually to the graduating candidate who has demonstrated academic excellence in an honours degree, and who is deemed by a Department to have achieved the highest standing in a Plan offered by that Department.
Former PhD student, Dr. Ali Kara has received the 2022 Cecil Graham Doctoral Dissertation Award from the . Dr. Kara completed his PhD at Queen’s University in 2021 under the supervision of Professor Serdar Yuksel. The award consists of a prize of $1000, a commemorative plaque and an invitation to speak at the annual meeting in the year of the award. Dr.
Masters student Adam Gronowski received the Canadian Society for Information Theory (CSIT) Paper Award for the best paper accepted at the Seventeenth IEEE Canadian Workshop on Information Theory, Ottawa, June 5-8, 2022.
The paper is entitled Rényi Fair Information Bottleneck for Image Classification by A. Gronowski, W. Paul (APL, Johns Hopkins Univ.), F. Alajaji, B. Gharesifard and P. Burlina (APL, Johns Hopkins Univ.). The paper consists of a certificate and a $1000 prize.
Date
Monday April 11, 2022Location
Online via ZoomMonday, April 11th, 2022
Time: 4:30 p.m. Place: Online via Zoom (contact Kaveh Mousavand for Zoom link)
Speaker: Emily Cliff (Universite de Sherbrooke)
Title: Moduli spaces of principal 2-group bundles and a categorification of the Freed-Quinn line bundle
Abstract: A 2-group is a higher categorical analogue of a group, while a smooth 2-group is a higher categorical analogue of a Lie group. An important example is the string 2-group in the sense of Schommer-Pries. We study the notion of principal bundles for smooth 2-groups, and investigate the moduli "space" of such objects. In particular in the case of flat principal bundles for a finite 2-group over a Riemann surface, we prove that the moduli space gives a categorification of the Freed--Quinn line bundle. This line bundle has as its global sections the state space of Chern--Simons theory for the underlying finite group. We can also use our results to better understand the notion of geometric string structures (as previously studied by Waldorf and Stolz--Teichner). This is based on joint work with Dan Berwick-Evans, Laura Murray, Apurva Nakade, and Emma Phillips.
Website details here:
Date
Friday April 8, 2022Location
Online (via Zoom)
Friday, April 8th, 2022
Time: 2:30 p.m. Place: Online (via Zoom)
Speaker: Runmin Wang (Southern Methodist University)
Title: Statistical Inference for Change Points in High-Dimensional Data
Abstract: Estimation and testing of change points in high-dimensional data have wide applications in many disciplines, such as biological science, economics and finance. In this talk, we introduce a new U-statistic based approach to both problems and show its advantage over several existing methods via theory and simulations. The talk consists of two parts. In the first part, we will introduce a new test based on U-statistics for testing a mean shift in high-dimensional data. The test aims to detect dense alternatives and is tuning parameter free. At the core of our theory, we show weak convergence of a sequential U-statistic based process, and derive the limiting distribution under both the null and alternatives. In the second part, we will discuss a change point location estimator which maximizes a new U-statistic based objective function. Under mild and easily interpretable assumptions, we derive its convergence rate and asymptotic distribution after suitable centering and normalization. A comparison with the popular least squares based approach illustrates the theoretical advantage of ours. A bootstrap-based approach is also proposed to construct a confidence interval with accurate coverage, which is corroborated by simulation results. We shall illustrate our method using a real data example at the end of the talk.
Runmin Wang is an Assistant Professor in the Department of Statistical Science at Southern Methodist University. He got his Ph.D. in Statistics from the University of Illinois at Urbana-Champaign in 2020. His research interests include change point inference, high-dimensional data and time series analysis.
Date
Monday April 4, 2022Location
Online via ZoomMonday, April 4th, 2022
Time: 4:30 p.m. Place: Online via Zoom (contact Kaveh Mousavand for Zoom link)
Speaker: Matthew Mastroeni (Iowa State University)
Title: Chow rings of matroids are Koszul
Abstract: The Chow ring of an algebraic variety is an algebro-geometric analog of the cohomology ring of a smooth manifold that encodes important information about the intersections between its subvarieties. Feichtner and Yuzvinsky computed a presentation for the Chow ring of a smooth toric variety associated to a matroid (and some other data) which is now called the Chow ring of the matroid. These rings have garnered significant attention in recent years thanks to their role in establishing long-standing conjectures on the combinatorics of matroids, including the resolution of the Heron-Rota-Welsh Conjecture by Adiprasito, Huh, and Katz and the resolution of the Top-Heavy Conjecture by Braden, Huh, Matherne, Proudfoot, and Wang.
From a commutative algebra standpoint, Chow rings of matroids are very nice graded Artinian Gorenstein rings defined by quadratic relations, and so, a natural conjecture posed by Dotsenko is that the Chow ring of a matroid is always Koszul. In this talk, we will discuss how the combinatorics of a matroid influences algebraic properties of its Chow ring, culminating in recent joint work with Jason McCullough giving an affirmative answer to Dotsenko’s conjecture.
Website details here:
Date
Wednesday March 30, 2022Location
Online via ZoomWednesday, March 30th, 2022
Time: 10:00 a.m. Place: Online via Zoom (contact Brian Ling for Zoom link)
Speaker: Chuan-Fa Tang (Department of Mathematical Sciences at University of Texas at Dallas)
Title: Taylor's law for semivariance and higher moments of heavy-tailed distributions
Abstract: The power law relates the population mean and variance is known as Taylor's law proposed by Taylor in 1961. We generalize Taylor's law from the light-tailed distributions to heavy-tailed distribution with infinite mean. Instead of population moments, we consider the power-law between the sample mean and many other sample statistics, such as the sample upper and lower semivariance, the skewness, the kurtosis, and higher moments of a random sample. We show that, as the sample size increases, the preceding sample statistics increase asymptotically in direct proportion to the power of the sample mean. These power laws characterize the asymptotic behavior of commonly used measures of the risk-adjusted performance of investments, such as the Sortino ratio, the Sharpe ratio, the potential upside ratio, and the Farinelli-Tibiletti ratio, when returns follow a heavy-tailed nonnegative distribution. In addition, we find the asymptotic distribution and moments of the number of observations exceeding the sample mean. We propose estimators of tail-index based on these scaling laws and the number of observations exceeding the sample mean and compare these estimators with some prior estimators.
Date
Monday March 28, 2022Location
Jeffery Hall Room 222 or Online via ZoomMonday, March 28th, 2022
Time: 11:00 a.m. Place: Jeffery Hall Room 222, or Online via Zoom (contact Deepanshu Prasad for Zoom link)
Speaker: Deepanshu Prasad
Title: Broken Circuit Complexes for a Matroids
Abstract: I will construct a standard $\mathcal{K}$-basis for the Orlik-Solomon algebra $A(\mathcal{A})$ by using broken circuit module and hence, prove that the Orlik-Solomon algebra is a free $\mathcal{K}$-module.
Date
Friday April 1, 2022Location
Online (via Zoom)
Friday, April 1st, 2022
Time: 2:30 p.m. Place: Online (via Zoom)
Speaker: Timothy Chan (University of Toronto)
Title: An Inverse Optimization Approach to Measuring Clinical Pathway Concordance
Abstract: Clinical pathways outline standardized processes in the delivery of care for a specific disease. Patient journeys through the healthcare system, however, can deviate substantially from these pathways. Given the positive benefits of clinical pathways, it is important to measure the concordance of patient pathways so that variations in health system performance or bottlenecks in the delivery of care can be detected, monitored, and acted upon. This paper proposes the first data-driven inverse optimization approach to measuring pathway concordance in any problem context. Our specific application considers clinical pathway concordance for stage III colon cancer. We develop a novel concordance metric and demonstrate using real patient data from Ontario, Canada that it has a statistically significant association with survival. Our methodological approach considers a patient’s journey as a walk in a directed graph, where the costs on the arcs are derived by solving an inverse shortest path problem. The inverse optimization model uses two sources of information to find the arc costs: reference pathways developed by a provincial cancer agency (primary) and data from real-world patient-related activity from patients with both positive and negative clinical outcomes (secondary). Thus, our inverse optimization framework extends existing models by including data points of both varying “primacy” and “alignment”. Data primacy is addressed through a two-stage approach to imputing the cost vector, whereas data alignment is addressed by a hybrid objective function that aims to minimize and maximize suboptimality error for different subsets of input data.
Timothy Chan is the Canada Research Chair in Novel Optimization and Analytics in Health, a Professor in the department of Mechanical and Industrial Engineering, the Director of the Centre for Analytics and AI Engineering, the Associate Director, Research and Thematic Programming of the Data Sciences Institute, and a Senior Fellow of Massey College at the University of Toronto. His primary research interests are in operations research, optimization, and applied machine learning, with applications in healthcare, medicine, sustainability, and sports.
