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X-WR-CALNAME:Department of Statistics
X-ORIGINAL-URL:https://statistics.sciences.ncsu.edu
X-WR-CALDESC:Events for Department of Statistics
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TZID:America/New_York
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DTSTART:20250309T070000
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DTSTART:20260308T070000
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20260206
DTEND;VALUE=DATE:20260207
DTSTAMP:20260417T124546
CREATED:20260113T161431Z
LAST-MODIFIED:20260113T161431Z
UID:29211-1770336000-1770422399@statistics.sciences.ncsu.edu
SUMMARY:Triangle Sports Analytics Competition 2026: Submissions Due
DESCRIPTION:Competition Website
URL:https://statistics.sciences.ncsu.edu/event/triangle-sports-analytics-competition-2026-submissions-due/
LOCATION:NC
CATEGORIES:Department,Graduate,Undergraduate
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260206T110000
DTEND;TZID=America/New_York:20260206T120000
DTSTAMP:20260417T124546
CREATED:20251215T135146Z
LAST-MODIFIED:20260127T160935Z
UID:29010-1770375600-1770379200@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 232A Withers Hall\, NC State Main Campus \nTitle: Rotated Mean-Field Variational Inference and Iterative Gaussianization \nPresenter: Sifan Liu \nAbstract: Mean-field variational inference (MFVI) approximates a target distribution with a product distribution in the standard coordinate system\, offering a scalable approach to Bayesian inference but often severely underestimating uncertainty due to neglected dependence. We show that MFVI can be greatly improved when performed along carefully chosen principal component axes rather than the standard coordinates. The principal components are obtained from a cross-covariance matrix of the target’s score function and identify orthogonal directions that capture the dominant discrepancies between the target distribution and a Gaussian reference. \nPerforming MFVI in a rotated system defines a rotation followed by a coordinatewise transformation that moves the target closer to Gaussian. Iterating this procedure yields a sequence of transformations that progressively Gaussianize the target. The resulting algorithm provides a computationally efficient construction of normalizing flows\, requiring only MFVI sub-problems and avoiding large-scale optimization. In posterior sampling tasks\, we demonstrate that the proposed method greatly outperforms standard MFVI while achieving accuracy comparable to normalizing flows at a much lower computational cost. \n 
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-46/
LOCATION:Withers Hall 232A
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260213T110000
DTEND;TZID=America/New_York:20260213T120000
DTSTAMP:20260417T124546
CREATED:20251215T135134Z
LAST-MODIFIED:20260120T203838Z
UID:29012-1770980400-1770984000@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 232A Withers Hall\, NC State Main Campus \nTitle: Generalized Bayesian Inference for Dynamic Random Dot Product Graphs \nPresenter: Josh Loyal \nAbstract: The random dot product graph is a popular model for network data with extensions that accommodate dynamic (time-varying) networks. However\, two significant deficiencies exist in the dynamic random dot product graph literature:  (1) no coherent Bayesian way to update one’s prior beliefs about the latent positions in dynamic random dot product graphs due to their complicated constraints\, and (2) no approach to forecast future networks with meaningful uncertainty quantification. This work proposes a generalized Bayesian framework that addresses these needs using a Gibbs posterior that represents a coherent updating of Bayesian beliefs based on a least-squares loss function. We establish the consistency and contraction rate of this Gibbs posterior under commonly adopted Gaussian random walk priors. For estimation\, we develop a fast Gibbs sampler with a time complexity for sampling the latent positions that is linear in the observed edges in the dynamic network\, which is substantially faster than existing exact samplers. Simulations and an application to forecasting international conflicts show that the proposed method’s in-sample and forecasting performance outperforms competitors. \n 
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-45/
LOCATION:Withers Hall 232A
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260217
DTEND;VALUE=DATE:20260218
DTSTAMP:20260417T124546
CREATED:20251208T155532Z
LAST-MODIFIED:20251208T155532Z
UID:28969-1771286400-1771372799@statistics.sciences.ncsu.edu
SUMMARY:No Classes - Wellness Day
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/no-classes-wellness-day-7/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260223T170000
DTEND;TZID=America/New_York:20260223T183000
DTSTAMP:20260417T124546
CREATED:20260205T141306Z
LAST-MODIFIED:20260205T141306Z
UID:29246-1771866000-1771871400@statistics.sciences.ncsu.edu
SUMMARY:February Professional Development Workshop
DESCRIPTION:Join us for the February Professional Development Workshop\, featuring a panel discussion with professionals from DLH Corporation. \n📅 Date: Monday\, February 23\, 2026⏰ Time: 5:00 – 6:30 PM📍 Location: 5104 SAS Hall Commons \nThis session will bring together a panel of industry professionals to discuss how statistics and data science are applied in industry settings\, particularly in areas related to health\, national security\, and applied research. Panelists will share insights into their career paths\, the types of roles available for students with quantitative backgrounds\, and what organizations like DLH look for when hiring new graduates. \nThe workshop will include a moderated panel discussion followed by time for student questions. \nWe hope you’ll join us for this opportunity to learn more about industry careers and engage directly with professionals working in the field.
URL:https://statistics.sciences.ncsu.edu/event/february-professional-development-workshop/
LOCATION:5104 SAS Hall (Solomon Commons)\, NC\, United States
CATEGORIES:College of Sciences Calendar,Department,Graduate,Undergraduate
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260227T110000
DTEND;TZID=America/New_York:20260227T120000
DTSTAMP:20260417T124546
CREATED:20251215T135131Z
LAST-MODIFIED:20260121T152017Z
UID:29011-1772190000-1772193600@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 232A Withers Hall\, NC State Main Campus \nTitle: Vertex alignment and changepoint localization in network time series \nPresenter: Zachary Lubberts \nAbstract: \nExisting methodology for changepoint localization in an evolving time series of networks generally relies on accurately prescribed vertex correspondence between network realizations at different times. However\, such vertex alignments are often misspecified or even unknown. To understand the impact of vertex misalignment on inference for dynamic networks\, two illustrative models are constructed for network evolution\, each with a similar changepoint. Different techniques are compared for changepoint localization\, ranging from the simple network statistic of average degree to the more involved and recently developed procedure of Euclidean mirrors. In one model\, vertex misalignment causes comparatively little error\, and in the other\, it seriously impairs localization\, although the Euclidean mirror procedure can nevertheless extract a meaningful signal. It is shown how misalignment between network realizations at different times can effectively weaken their underlying correlation\, impeding inference procedures that rely on accurate inference of such correlation. Graph matching and optimal transport is discussed\, both of which are potential mechanisms for mitigating errors from misalignment\, but which may also fail to improve inference under certain models. Simulations are presented that illustrate these varying effects on approaches to localization.\n\n 
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-44/
LOCATION:Withers Hall 232A
CATEGORIES:College of Sciences Calendar,Department,Faculty,Seminars
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