Friday, January 29th, 2021

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

Speaker: Milen Yakimov (Northeastern University)

Title: Noncommutative Discriminants.

Abstract: The notion of discriminant plays an important role in various algebraic, geometric and combinatorial settings. The discriminant of a noncommutative algebra is modeled on Dedekind's definition for algebraic number fields. The discriminants in the former class have many applications but have only been computed in few situations. We will present an introduction to this subject and will then describe three general theorems for computing discriminants of noncommutative algebras based on Poisson geometry, Representation Theory and Cluster Algebras, respectively. The three theorems can be applied to compute the discriminants of many families of algebras of wide interest: quantum matrices at roots of unity, quantum Weyl algebras, quantum Schubert cell algebras, algebras in noncommutative projective algebraic geometry and others. The talk is based on joint work with Kenneth Brown (Glasgow University), Bach Nguyen (Xavier University) and Kurt Trampel (University of Notre Dame).

Milen Yakimov is a Professor in the Department of Mathematics at Northeastern University. His research interests include noncommutative algebra, quantum groups, Poisson geometry, cluster algebras, representation theory and integrable systems. Before joining Northeastern University, he was the Michael F. and Roberta Nesbit McDonald Professor in the Department of Mathematics at the Louisiana State University. He became a Fellow of the American Mathematical Society in 2018.

Friday, January 15th, 2021

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

Speaker: Grace Y. Yi (University of Western Ontario)

Title: Characterizing the Dynamic of COVID-19 with a New Epidemic Model.

Abstract: The mystery of the coronavirus disease 2019 (COVID-19) and the lack of effective treatment for COVID-19 have presented a strikingly negative impact on public health. While research on COVID-19 has been ramping up rapidly, a very important yet somewhat overlooked challenge is on the quality and unique features of COVID-19 data. The manifestations of COVID-19 are not yet well understood. The swift spread of the virus is largely attributed to its stealthy transmissions in which infected patients may be asymptomatic or exhibit only flu-like symptoms in the early stage. Due to the limited test resources and a good portion of asymptomatic infections, the confirmed cases are typically under-reported, error-contaminated, and involved with substantial noise. In this talk, I will discuss some issues related to faulty COVID-19 data and present a new model to describe the dynamic evolution of COVID-19. In addition, I will mention a website of COVID-19 Canada (https://covid-19-canada.uwo.ca/), developed by the team co-led by Dr. Wenqing He and myself, which provides a comprehensive and real-time visualization of the Canadian COVID-19 data.

Grace Y. Yi is a professor of the University of Western Ontario where she currently holds a Tier I Canada Research Chair in Data Science. She received her Ph.D. in Statistics from the University of Toronto in 2000 and then joined the University of Waterloo as a postdoctoral fellow (2000-2001), Assistant Professor (2001-2004), Associate Professor (2004-2010), Professor (2010-2019), and University Research Chair (2011-2018). Her research interests focus on developing methodology to address various challenges concerning Data Science, public health, cancer research, epidemiological studies, environmental studies, and social science. Her recent research has been centered around investigating machine learning and statistical methods to tackle problems concerning imaging data, missing data, measurement error in variables, causal inference, high dimensional data, survival data, and longitudinal data. She is a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, and an Elected Member of the International Statistical Institute. She won the prestigious CRM–SSC Prize of the Statistical Society of Canada in 2010. She was a recipient of the University Faculty Award (2004-2009) granted by the Natural Sciences and Engineering Research Council of Canada. Her work with Xianming Tan and Runze Li won the Canadian Journal of Statistics Award for 2016. She has served the professions in various capacities. She was the Editor-in-Chief of The Canadian Journal of Statistics (2016-2018) as well as the President of the Biostatistics Section of the Statistical Society of Canada in 2016, and the Founder of the first chapter (Canada Chapter, established in 2012) of International Chinese Statistical Association (ICSA).

Monday, December 7th, 2020

Time: 3:00-4:00p.m.  Place: Online (register)

Speaker: Dr. Margaret E. Roberts
Associate Professor
Department of Political Science and the Halıcıoğlu Data Science Institute
University of California, San Diego

Title: Resilience to Online Censorship

Free Event | All Welcome | Register Here

Abstract: 
To what extent are Internet users resilient to online censorship? When does censorship influence consumption of information and when does it create backlash? Drawing on data reflecting censorship evasion of the Great Firewall of China, I examine the extent to which individuals affected by censorship seek out ways to route around it. Using censorship events of Wikipedia and Instagram and crisis events like the outbreak of COVID, I examine how changes in the censorship and political environment influence censorship evasion. I find that crisis, as well as censorship of very popular and addictive websites, can create incentives for censorship evasion that in turn provides a gateway to long censored and sensitive political information. But, in the absence of a strong incentive to jump the wall, censorship events cut off access not only to political information, but also to opportunities for exploration and learning. Based on joint work with Jennifer Pan, Will Hobbs, Keng-Chi Chang, and Zachary Steinert-Threlkeld.

Profile: 
Dr. Roberts is Associate Professor in the Department of Political Science and the Halıcıoğlu Data Science Institute at the University of California, San Diego. She is also part of the Omni-Methods Group. Her research interests lie in the intersection of political methodology and the politics of information, with a specific focus on methods of automated content analysis and the politics of censorship and propaganda in China. Roberts received a PhD from Harvard in Government (2014), a MS from Stanford in Statistics (2009), and BA from Stanford in International Relations and Economics (2009). Much of her research uses social media, online experiments, and large collections of texts to understand the influence of censorship and propaganda on access to information and beliefs about politics. Her book, Censored: Distraction and Diversion Inside China’s Great Firewall, published by Princeton University Press in 2018, was listed as one of the Foreign Affairs Best Books of 2018, was honoured with the Goldsmith Book Award, and has been awarded the Best Book Award in the Human Rights Section and Information Technology and Politics Section of the American Political Science Association. She also holds a Chancellor’s Associates Endowed Chair at UCSD.

ARES is a collaboration between CANSSI Ontario and its partner universities.

Friday, November 27th, 2020

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

Speaker: Ian Frankel (Queen's University)

Title: Invariant measures for straight line flows.

Abstract: We discuss the question of equidistribution of billiard trajectories in polygons. As it turns out, for polygons whose angles are rational, this is related to the geometry of a 1-parameter family of surfaces in a moduli space. We will describe how the possible measures with respect to which a billiard trajectory may equidistribute are constrained by this 1-parameter family of surfaces.

Ian Frankel is a Coleman Research Fellow within the Department of Mathematics and Statistics at Queen’s University. He obtained his Ph.D. in Mathematics from the University of Chicago in 2018. He was a Research Fellow at the Higher School of Economics and a Fields Postdoctoral Fellow at The Fields Institute for Research in Mathematical Sciences. He is mainly interested in geometry and topology and dynamical systems.

Friday, November 13th, 2020

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

Speaker: Yaiza Canzani (University of North Carolina)

Title: Eigenfunction concentration via geodesic beams.

Abstract: A vast array of physical phenomena, ranging from the propagation of waves to the location of quantum particles, is dictated by the behavior of Laplace eigenfunctions. Because of this, it is crucial to understand how various measures of eigenfunction concentration respond to the background dynamics of the geodesic flow. In collaboration with J. Galkowski, we developed a framework to approach this problem that hinges on decomposing eigenfunctions into geodesic beams. In this talk, I will present these techniques and explain how to use them to obtain quantitative improvements on the standard estimates for the eigenfunction's pointwise behavior, Lp norms, and for both pointwise and integrated Weyl Laws. One consequence of this method is a quantitatively improved Weyl Law for the eigenvalue counting function on all product manifolds.

Yaiza Canzani is an Assistant Professor in the Department of Mathematics at the University of North Carolina at Chapel Hill. She was awarded a Sloan Research Fellowship in 2018. Before joining UNC, Prof. Canzani was a Benjamin Peirce Fellow at Harvard University and a member of the Institute for Advanced Study. She obtained her Ph.D. from McGill University in 2013 under the supervision of Dmitry Jakobson and John Toth. She works on geometric analysis and spectral theory.