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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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BEGIN:VTIMEZONE
TZID:America/New_York
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DTSTART:20250309T070000
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DTSTART:20251102T060000
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DTSTART:20260308T070000
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20260101
DTEND;VALUE=DATE:20260102
DTSTAMP:20260416T153659
CREATED:20251208T154746Z
LAST-MODIFIED:20251208T155105Z
UID:28963-1767225600-1767311999@statistics.sciences.ncsu.edu
SUMMARY:University Closed - Winter Break
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/university-closed-winter-break-7/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260112
DTEND;VALUE=DATE:20260113
DTSTAMP:20260416T153700
CREATED:20251208T155253Z
LAST-MODIFIED:20251208T155346Z
UID:28965-1768176000-1768262399@statistics.sciences.ncsu.edu
SUMMARY:Classes Resume - Spring 2026
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/classes-resume-spring-2026/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260119
DTEND;VALUE=DATE:20260120
DTSTAMP:20260416T153700
CREATED:20251208T155443Z
LAST-MODIFIED:20251208T155443Z
UID:28967-1768780800-1768867199@statistics.sciences.ncsu.edu
SUMMARY:No Classes - Holiday
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/no-classes-holiday-12/
LOCATION:NC
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260122T160000
DTEND;TZID=America/New_York:20260122T170000
DTSTAMP:20260416T153700
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
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260123T110000
DTEND;TZID=America/New_York:20260123T120000
DTSTAMP:20260416T153700
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:20260126T170000
DTEND;TZID=America/New_York:20260126T183000
DTSTAMP:20260416T153700
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:20260130T110000
DTEND;TZID=America/New_York:20260130T120000
DTSTAMP:20260416T153700
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
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