Date
Thursday March 21, 2024Location
Jeffery Hall, Room 319Thursday, March 21st, 2024
Time: 4:00 p.m. Place: Jeffery Hall, Room 319
Speaker: Calvin Fletcher
Title: Further examples of finite type
Abstract: Last week we explored examples of cluster algebras of finite type, specifically type A_n and B_n. This week, we will see some settings in which type C_n and D_n arise. To conclude, we will introduce the Starfish lemma.
Date
Friday March 22, 2024Location
Jeffery Hall, Room 234Friday, March 22, 2024
Time: 2:30 p.m. Place: Jeffery Hall, Room 234
Speaker: Xiao-Li Meng (Harvard)
Title: Multi-resolution Meandering: Personalized Treatments, Individual Privacy, Machine Unlearning, and aWorld without Randomness
Abstract: Data science revolutionizes the granularity of human inquiries and even offers the promise of personalized assessments. However, how can we assess individual treatment effect before treating the individual? Transitional Inference addresses this dilemma through the concept of 鈥渢ransfer to the similar,鈥 a notion that has been pondered by philosophers since Galen of the Roman Empire. This talk presents a Multi-Resolution Framework (Li and Meng, 2021, JASA) for transitional inference, where similarity is prescribed probabilistically by concomitantly specifying the sameness 鈥 the shared distributional form 鈥 and the differences 鈥 the individual realizations. This framework avoids the concept of randomness and defines 鈥渋ndividual probability鈥 as a deterministic limit with infinite resolution. These conceptualizations help us operationalize the meaning of personalized treatments, clarify what individual privacy is protected by differential privacy, and anticipate the challenges of preserving an individual鈥檚 right to be forgotten through machine unlearning. Furthermore, it reveals a world that is resistant to overfitting when the resolutions of our data and (deep) learning far exceed the resolution necessary for pattern recognition.
Bio: Xiao-Li Meng, the Founding Editor-in-Chief of Harvard Data Science Review and the Whipple V. N. Jones Professor of Statistics at Harvard University, is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by the Committee of Presidents of Statistical Societies (COPSS) in 2001, and he is the recipient of numerous awards and honours for his more than 150 publications. In 2020, he was elected to the American Academy of Arts and Sciences. Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard, where he served as the Chair of the Department of Statistics (2004鈥2012) and the Dean of the Graduate School of Arts and Sciences (2012鈥2017).
Date
Thursday March 14, 2024Location
Jeffery Hall, Room 319Thursday, March 14th, 2024
Time: 4:00 p.m. Place: Jeffery Hall, Room 319
Speaker: Julia McClellan
Title: Cluster Algebras and Coordinate Rings
Abstract: In this talk we will revisit our previous result that under certain conditions, the coordinate ring of an algebraic variety can be naturally identified with a cluster algebra. We will then use the familiar example of the Pl眉cker ring to see that it can carry two non-isomorphic cluster structures of classical types 鈥 A_m and B_m.
Date
Thursday March 14, 2024Location
Jeffery Hall, Room 118Thursday, March 14th, 2024
Time: 5:30 p.m. Place: Jeffery Hall, Room 118
Speaker: James Mingo (Queen's University)
Title: Up and down and down and up
Abstract: We look at the number of ways of arranging 1, 2, 3, $\ldots$, $n$, so that the numbers go up, then down, then back up, and so on. While there isn鈥檛 a simple closed formula for this number as a function of $n$, there is a very simple way of analyzing the number and a simple connection to the Taylor series of some very familiar functions.
Date
Monday March 11, 2024Location
Jeffery Hall, Room 202Monday, March 11th, 2024
Time: 2:30 p.m. Place: Jeffery Hall, Room 202
Speaker: Nic Fellini (Queen's University)
Title: Diophantine equations, linear recurrences, and p-adic analysis
Abstract: In some instances, problems from Diophantine equations can be translated into problems concerning linear recurrences. A question that arises through this connection is the following: If (u_n) is a sequence solving an r-th order linear recurrence relation with integer coefficients, what does the zero set {u_n = 0} "look" like? In a surprising twist, the only known solutions to this question are p-adic in nature. The goal for this talk is to give an example driven explanation of how we can translate problems from Diophantine equations to studying the sets {u_n = a} using p-adic analysis.
Date
Thursday March 7, 2024Location
Jeffery Hall, Room 319Thursday, March 7th, 2024
Time: 4:00 p.m. Place: Jeffery Hall, Room 319
Speaker: Sonja Ruzic
Title: Commutative rings with a cluster algebra structure.
Abstract: In this talk, we look at several examples of commutative rings which have an inherent cluster algebra structure. In particular, we will see different explicit examples of commutative rings which are also cluster algebras of finite type.
Date
Thursday March 7, 2024Location
Jeffery Hall, Room 118Thursday, March 7th, 2024
Time: 5:30 p.m. Place: Jeffery Hall, Room 118
Speaker: Yanglei Song (Queen's University)
Title: Stein's Paradox, Lemma, and Method
Abstract: Consider the estimation of three unrelated quantities, such as the speed of light, your daily coffee consumption, and Kingston's COVID activity level. The James鈥揝tein estimator proposes to estimate them simultaneously and borrow information across them. For normally distributed data, it beats, in terms of the sum of quadratic losses, the most natural approach, which estimates each quantity individually. We prove this counter-intuitive result using Stein's lemma.
The reverse direction of Stein's lemma characterizes the standard normal distribution, It motivates a family of powerful techniques now called Stein's method for bounding distances between probability distributions. We discuss the basic idea behind the method.
Date
Friday March 8, 2024Location
Jeffery Hall, Room 234Friday, March 8, 2024
Time: 2:30 p.m. Place: Jeffery Hall, Room 234
Speaker: Wei Tu (Queen's University)
Title: Informative Censoring in Oncology Clinical Trials
Abstract: Clinical trials remain the cornerstone for evaluating new cancer treatments, with the selection of primary endpoints being crucial to the trial鈥檚 design and potential to change medical practice. Progression-free survival (PFS) is frequently chosen as an endpoint in oncology trials due to the advantage of shorter study durations, particularly in studies of early-stage diseases. Nevertheless, informative censoring presents a challenge in trials that use PFS as the primary endpoint. This type of censoring occurs when the projected outcomes for individuals who leave the study early differ from those who continue, which can skew the trial鈥檚 findings. In this talk, I will discuss the consequences of informative censoring and the statistical methods used to mitigate its effects, using data from the Canadian Cancer Trials Group (CCTG) MA.17R trial as an example. To assess informative censoring鈥檚 effect on estimated treatment benefits, we evaluate the use of competing risk assessments and gamma imputation methods for handling non-breast cancer mortality within the dataset. Moreover, we utilize inverse probability censoring weighting (IPCW) to mitigate its impact, assigning weights to participants based on shared characteristics to accurately represent those lost to on-breast cancer causes.
Bio: Dr. Tu is a Senior Biostatistician with the Canadian Cancer Trials Group and an Assistant Professor in the Department of Public Health Sciences at 黑料吃瓜资源. He completed both his MSc and PhD in Statistics at the University of Alberta. His research interests encompass clinical trial design and analysis, statistical machine learning, data privacy, and genomic data science. His work is supported by the Natural Sciences and Engineering Research Council of Canada, Canadian Institutes of Health Research and Canadian Cancer Society.
Date
Monday March 4, 2024Location
Jeffery Hall, Room 202Monday, March 4th, 2024
Time: 2:30 p.m. Place: Jeffery Hall, Room 202
Speaker: Sunil Naik (Queen's University)
Title: A note on Dowling lattices
Abstract: In this talk, we will discuss the general framework of sieve methods for geometric lattices. The special case of Dowling lattices leads us to certain sums involving Whitney numbers of these lattices, which we will estimate using tools from complex analysis. This is an ongoing joint work with Prof. M. Ram Murty.
Date
Tuesday March 5, 2024Location
Jeffery Hall, Room 319 (Via Zoom)Tuesday, March 5th, 2024
Time: 9:30 a.m. Place: Jeffery Hall, Room 319 (Via Zoom)
Speaker: Ugo Piomelli (黑料吃瓜资源)
Title: Numerical simultions of turbulence
Abstract: Turbulence has been described as the last unsolved problem of classical mechanics. It is a phenomenon that affects most fluid flows in engineering and the natural sciences, and its understanding is crucial to improve the design of ship, aircraft, and cars, the prediction of weather patterns, or the impact of environmental changes. Turbulence is a multiscale phenomenon, and is neither entirely deterministic, nor completely stochastic. While statistical mechanics tools can be used to predict the behaviour of the small and intermediate-sized motions, no theory has fully explained the behaviour of the larger ones, which depend strongly on the boundary conditions. Yet, it is the larger scales that drive the momentum and energy transport.
The numerical solution of the equations of motion, the Navier-Stokes equations, is a viable alternative to theory; however, the computational resources required for simulations of complex flows have been a limiting factor. The increased availability of computational resources and the development of new algorithms has made it possible, in recent years, to approach more realistic problem. High-fidelity simulations have resulted in better understanding of the physics, and, in turn, in the development of more accurate lower-level models for industrial design.
In this talk the challenges associated with the simulation of turbulent flows will be discussed. The most common numerical methods will be presented, with their advantages and disadvantages. A series of illustrative examples from Piomelli's research group will then be presented.
(Short) Bio: Ugo Piomelli obtained a Laurea in Ingegneria Aeronautica from the Universit脿 di Napoli "Federico II" in 1979. He then earned a Master of Science Degree in Aerospace Engineering from the University of Notre Dame in 1984 and a PhD in Mechanical Engineering from Stanford University in 1988. From 1987 to 2008 he was on the faculty of the Department of Mechanical Engineering at the University of Maryland, first as Assistant, then Associate and finally Full Professor. He served as Associate Chair and Director of Graduate studies from 2002 to 2007. In August 2008 he joined the Department of Mechanical and Materials Engineering at 黑料吃瓜资源 in Kingston, Ontario, where he held, from 2008 to 2022, the Tier 1 Canada Research Chair in Turbulence Simulation and Modelling.
Professor Piomelli has published over 100 refereed journal articles in the fields of turbulence and transition modelling and simulation. His work has been cited over 24,400 times, and he has an h-index of 55 (Google Scholar). He was elected Fellow of the Royal Society of Canada in 2015, of the Canadian Academy of Engineering in 2021, of the American Society of Mechanical Engineers in 2009, of the Institute of Physics (UK) in 2004 and of the American Physical Society in 2002. Since 2015, he has been the Editor-in-Chief of the Journal of Turbulence. His present research includes studies of the flow in rivers and lakes, turbulent boundary layers over smooth and rough surfaces, model development for large-eddy simulations, and flows in hydro-electric turbines and aeronautical applications.
