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METHOD:PUBLISH
X-WR-CALNAME:Department of Statistics
X-ORIGINAL-URL:https://statistics.sciences.ncsu.edu
X-WR-CALDESC:Events for Department of Statistics
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
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BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
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TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
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TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
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BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
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END:VTIMEZONE
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260703
DTEND;VALUE=DATE:20260704
DTSTAMP:20260108T180800Z
CREATED:20260108T180800Z
LAST-MODIFIED:20260108T180800Z
UID:29107-1783036800-1783123199@statistics.sciences.ncsu.edu
SUMMARY:Independence Day Observed (University Closed)
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/independence-day-observed-university-closed/
LOCATION:NC
CATEGORIES:College of Sciences Calendar,Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260619
DTEND;VALUE=DATE:20260620
DTSTAMP:20260108T180655Z
CREATED:20260108T180655Z
LAST-MODIFIED:20260108T180655Z
UID:29109-1781827200-1781913599@statistics.sciences.ncsu.edu
SUMMARY:Juneteenth - (No Classes)
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/juneteenth-no-classes/
LOCATION:NC
CATEGORIES:College of Sciences Calendar,Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260525
DTEND;VALUE=DATE:20260526
DTSTAMP:20260108T180557Z
CREATED:20260108T180557Z
LAST-MODIFIED:20260108T180557Z
UID:29101-1779667200-1779753599@statistics.sciences.ncsu.edu
SUMMARY:Memorial Day (University Closed)
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/memorial-day-university-closed/
LOCATION:NC
CATEGORIES:College of Sciences Calendar,Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260520
DTEND;VALUE=DATE:20260521
DTSTAMP:20260108T180148Z
CREATED:20260108T180148Z
LAST-MODIFIED:20260108T180148Z
UID:29099-1779235200-1779321599@statistics.sciences.ncsu.edu
SUMMARY:Summer Session - First day of classes
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/summer-session-first-day-of-classes-2/
LOCATION:NC
CATEGORIES:College of Sciences Calendar,Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260509
DTEND;VALUE=DATE:20260510
DTSTAMP:20251208T160642Z
CREATED:20251208T160642Z
LAST-MODIFIED:20251208T160642Z
UID:28992-1778284800-1778371199@statistics.sciences.ncsu.edu
SUMMARY:University Graduation
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/university-graduation-4/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260508T140000
DTEND;TZID=America/New_York:20260508T150000
DTSTAMP:20260507T154152Z
CREATED:20251208T161658Z
LAST-MODIFIED:20260507T154152Z
UID:29003-1778248800-1778252400@statistics.sciences.ncsu.edu
SUMMARY:Department Graduation
DESCRIPTION:stream live: https://ncsu.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=e85dd226-c7a0-43cf-8a8f-b43d00de5620 \n\n\n\nMcKimmon Center\, Room 2\n\n\n\n\n\n\nCeremony 2-3pm\, reception to follow
URL:https://statistics.sciences.ncsu.edu/event/department-graduation-2/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260506
DTEND;VALUE=DATE:20260507
DTSTAMP:20251208T160543Z
CREATED:20251208T160543Z
LAST-MODIFIED:20251208T160543Z
UID:28989-1778025600-1778111999@statistics.sciences.ncsu.edu
SUMMARY:Final Exams
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/final-exams-68/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260505
DTEND;VALUE=DATE:20260506
DTSTAMP:20251208T160356Z
CREATED:20251208T160356Z
LAST-MODIFIED:20251208T160356Z
UID:28985-1777939200-1778025599@statistics.sciences.ncsu.edu
SUMMARY:Final Exams
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/final-exams-66/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260504
DTEND;VALUE=DATE:20260505
DTSTAMP:20251208T160452Z
CREATED:20251208T160452Z
LAST-MODIFIED:20251208T160452Z
UID:28987-1777852800-1777939199@statistics.sciences.ncsu.edu
SUMMARY:Final Exams
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/final-exams-67/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260501
DTEND;VALUE=DATE:20260502
DTSTAMP:20251208T160309Z
CREATED:20251208T160309Z
LAST-MODIFIED:20251208T160309Z
UID:28983-1777593600-1777679999@statistics.sciences.ncsu.edu
SUMMARY:Final Exams
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/final-exams-65/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260430
DTEND;VALUE=DATE:20260501
DTSTAMP:20251208T160215Z
CREATED:20251208T160215Z
LAST-MODIFIED:20251208T160215Z
UID:28981-1777507200-1777593599@statistics.sciences.ncsu.edu
SUMMARY:Final Exams
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/final-exams-64/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260428T080000
DTEND;TZID=America/New_York:20260428T170000
DTSTAMP:20251208T160047Z
CREATED:20251208T160047Z
LAST-MODIFIED:20251208T160047Z
UID:28979-1777363200-1777395600@statistics.sciences.ncsu.edu
SUMMARY:Last Day -- Spring 2026
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/last-day-spring-2026/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260424T110000
DTEND;TZID=America/New_York:20260424T120000
DTSTAMP:20251215T150257Z
CREATED:20251215T150257Z
LAST-MODIFIED:20251215T150257Z
UID:29034-1777028400-1777032000@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar (Student Presentations)
DESCRIPTION:Location: 232A Withers Hall\, NC State Main Campus \nTitle:  \nPresenter:  TBD \nAbstract: \ndetails to come
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-student-presentations/
LOCATION:Withers Hall 232A
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260422T170000
DTEND;TZID=America/New_York:20260422T183000
DTSTAMP:20260324T211608Z
CREATED:20260324T211608Z
LAST-MODIFIED:20260324T211608Z
UID:29367-1776877200-1776882600@statistics.sciences.ncsu.edu
SUMMARY:Professional Development Workshop
DESCRIPTION:Practitioner Talk: Forecasting in the Wild \nA Q&A session with Harrison Katz of AirBnB
URL:https://statistics.sciences.ncsu.edu/event/professional-development-workshop-5/
LOCATION:5104 SAS Hall (Solomon Commons)\, NC\, United States
CATEGORIES:College of Sciences Calendar,Graduate,Undergraduate
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260403T110000
DTEND;TZID=America/New_York:20260403T120000
DTSTAMP:20260328T015317Z
CREATED:20251215T145641Z
LAST-MODIFIED:20260328T015317Z
UID:29027-1775214000-1775217600@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 232A Withers Hall\, NC State Main Campus \nTitle: Building faster and more expressive BART models \nPresenter: Sameer Deshpande \nAbstract: \nBayesian Additive Regression Trees (BART) is a highly effective nonparametric regression model that approximates unknown functions with a sum of binary regression trees. Most implementations of BART are based on trees that (i) recursively partition continuous inputs one variable at a time; (ii) one-hot encode categorical predictors; and (iii) represent piecewise constant functions. These implementations are fundamentally limited in their ability to learn complex decision boundaries that are not aligned with coordinate axes; to “borrow strength” across multiple groups; to leverage structural relationships between multiple categorical predictors (e.g.\, adjacency and nesting); and to estimate smooth functions.
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-49/
LOCATION:Withers Hall 232A
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260320
DTEND;VALUE=DATE:20260321
DTSTAMP:20251208T155920Z
CREATED:20251208T155920Z
LAST-MODIFIED:20251208T155920Z
UID:28977-1773964800-1774051199@statistics.sciences.ncsu.edu
SUMMARY:No Classes - Spring Break
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/no-classes-spring-break-5/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260318
DTEND;VALUE=DATE:20260319
DTSTAMP:20251208T155832Z
CREATED:20251208T155832Z
LAST-MODIFIED:20251208T155832Z
UID:28975-1773792000-1773878399@statistics.sciences.ncsu.edu
SUMMARY:No Classes - Spring Break
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/no-classes-spring-break-4/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260317
DTEND;VALUE=DATE:20260318
DTSTAMP:20251208T155750Z
CREATED:20251208T155750Z
LAST-MODIFIED:20251208T155750Z
UID:28973-1773705600-1773791999@statistics.sciences.ncsu.edu
SUMMARY:No Classes - Spring Break
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/no-classes-spring-break-3/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260316
DTEND;VALUE=DATE:20260317
DTSTAMP:20251208T155702Z
CREATED:20251208T155702Z
LAST-MODIFIED:20251208T155702Z
UID:28971-1773619200-1773705599@statistics.sciences.ncsu.edu
SUMMARY:No Classes - Spring Break
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/no-classes-spring-break-2/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260306T110000
DTEND;TZID=America/New_York:20260306T120000
DTSTAMP:20251215T150654Z
CREATED:20251215T145548Z
LAST-MODIFIED:20251215T150654Z
UID:29026-1772794800-1772798400@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 232A Withers Hall\, NC State Main Campus \nTitle:  \nPresenter: Li Ma \nAbstract: \ndetails to come
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-48/
LOCATION:Withers Hall 232A
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260227T110000
DTEND;TZID=America/New_York:20260227T120000
DTSTAMP:20260121T152017Z
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260223T170000
DTEND;TZID=America/New_York:20260223T183000
DTSTAMP:20260205T141306Z
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;VALUE=DATE:20260217
DTEND;VALUE=DATE:20260218
DTSTAMP:20251208T155532Z
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:20260213T110000
DTEND;TZID=America/New_York:20260213T120000
DTSTAMP:20260120T203838Z
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;TZID=America/New_York:20260206T110000
DTEND;TZID=America/New_York:20260206T120000
DTSTAMP:20260127T160935Z
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;VALUE=DATE:20260206
DTEND;VALUE=DATE:20260207
DTSTAMP:20260113T161431Z
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:20260130T110000
DTEND;TZID=America/New_York:20260130T120000
DTSTAMP:20260121T204720Z
CREATED:20260121T141202Z
LAST-MODIFIED:20260121T204720Z
UID:29225-1769770800-1769774400@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 232A Withers Hall\, NC State Main Campus \nTitle: Finding Anomalous Cliques in Inhomogeneous Networks using Egonets \nPresenter:  Srijan Sengupta \nAbstract: Cliques\, or fully connected subgraphs\, are among the most important and well-studied graph motifs in network science. We consider the problem of finding a statistically anomalous clique hidden in a large network. There are two parts to this problem: (1) detection\, i.e.\, determining whether an anomalous clique is present\, and (2) localization\, i.e.\, determining which vertices of the network constitute the detected clique. While this problem has been extensively studied under the homogeneous Erdos-Renyi model\, little progress has been made beyond this simple setting\, and no existing method can perform detection and localization in inhomogeneous networks within finite time. To address this gap\, we first show that in homogeneous networks\, the anomalousness of a clique depends solely on its size. This property does not carry over to inhomogeneous networks\, where the identity of the vertices forming the clique plays a critical role\, and a smaller clique can be more anomalous than a larger one. Building on this insight\, we propose a unified method for clique detection and localization based on a class of subgraphs called egonets. The proposed method generalizes to a wide variety of inhomogeneous network models and is naturally amenable to parallel computing. We establish the theoretical properties of the proposed method and demonstrate its empirical performance through simulation studies and application to two real world networks. \n 
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-51/
LOCATION:Withers Hall 232A
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260126T170000
DTEND;TZID=America/New_York:20260126T183000
DTSTAMP:20260113T153332Z
CREATED:20260113T153332Z
LAST-MODIFIED:20260113T153332Z
UID:29210-1769446800-1769452200@statistics.sciences.ncsu.edu
SUMMARY:Professional Development Workshop
DESCRIPTION:Welcome back! We hope you’re off to a great start to the spring semester. Join us for the first Professional Development Workshop of the calendar year as we kick off the semester with a panel focused on careers in public service. \nCareers in State Government: Applying Statistics and Data Science \n📅 Date: Monday\, January 26\, 2026\n⏰ Time: 5:00 – 6:30 PM\n📍 Location: 5104 SAS Hall Commons \nIn this session\, students will hear from representatives from the North Carolina Office of the State Auditor (NC OSA) in a panel discussion on how statistics and data science are applied in state government. Panelists will also share information about the NC OSA Internship Program and pathways for students interested in government careers. \nWe hope you’ll join us for this informative and timely conversation as we begin the spring semester.
URL:https://statistics.sciences.ncsu.edu/event/professional-development-workshop-4/
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:20260123T110000
DTEND;TZID=America/New_York:20260123T120000
DTSTAMP:20251215T144455Z
CREATED:20251215T135157Z
LAST-MODIFIED:20251215T144455Z
UID:29009-1769166000-1769169600@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 232A Withers Hall\, NC State Main Campus \nTitle: Multivariate spatial models for high-dimensional ecological data \nPresenter: Jeffrey W. Doser\, Ph.D. \nAbstract: The proliferation of big spatial data from autonomous monitoring systems\, national monitoring programs\, and citizen science platforms offers never-before-seen opportunities to address pressing natural resource management and conservation questions. Yet\, such massive spatial data present a variety of computational and statistical challenges that limit their use by practitioners. In this seminar\, I will discuss recent methodological and software advances that enable efficient modeling of multivariate spatial data where both the number of locations and number of outcomes at each location is large. Case studies motivated by ecological and forestry data will highlight the framework’s utility for informing natural resource management and conservation.
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-47/
LOCATION:Withers Hall 232A
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260122T160000
DTEND;TZID=America/New_York:20260122T170000
DTSTAMP:20260113T161522Z
CREATED:20260113T161522Z
LAST-MODIFIED:20260113T161522Z
UID:29214-1769097600-1769101200@statistics.sciences.ncsu.edu
SUMMARY:Triangle Sports Analytics Competition 2026: Virtual Information Session
DESCRIPTION:Zoom Link\nCompetition Website
URL:https://statistics.sciences.ncsu.edu/event/triangle-sports-analytics-competition-2026-virtual-information-session/
LOCATION:NC
CATEGORIES:Department,Graduate,Undergraduate
END:VEVENT
END:VCALENDAR