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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
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
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TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
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TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
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TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
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TZOFFSETTO:-0400
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DTSTART:20260308T070000
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DTSTART:20261101T060000
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20251230
DTEND;VALUE=DATE:20251231
DTSTAMP:20260421T030926
CREATED:20251208T154605Z
LAST-MODIFIED:20251208T154952Z
UID:28958-1767052800-1767139199@statistics.sciences.ncsu.edu
SUMMARY:University Closed - Winter Break
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/university-closed-winter-break-5/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251229
DTEND;VALUE=DATE:20251230
DTSTAMP:20260421T030926
CREATED:20251208T154501Z
LAST-MODIFIED:20251208T154501Z
UID:28956-1766966400-1767052799@statistics.sciences.ncsu.edu
SUMMARY:University Closed - Winter Break
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/university-closed-winter-break-4/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251226
DTEND;VALUE=DATE:20251227
DTSTAMP:20260421T030926
CREATED:20251208T154305Z
LAST-MODIFIED:20251208T154820Z
UID:28954-1766707200-1766793599@statistics.sciences.ncsu.edu
SUMMARY:University Closed - Winter Break
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/university-closed-winter-break-3/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251225
DTEND;VALUE=DATE:20251226
DTSTAMP:20260421T030926
CREATED:20251208T153935Z
LAST-MODIFIED:20251208T154342Z
UID:28952-1766620800-1766707199@statistics.sciences.ncsu.edu
SUMMARY:University Closed - Winter Break
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/university-closed-winter-break-2/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251224
DTEND;VALUE=DATE:20251225
DTSTAMP:20260421T030926
CREATED:20251208T153727Z
LAST-MODIFIED:20251208T153827Z
UID:28948-1766534400-1766620799@statistics.sciences.ncsu.edu
SUMMARY:University Closed - Winter Break
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/university-closed-winter-break/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251213
DTEND;VALUE=DATE:20251214
DTSTAMP:20260421T030926
CREATED:20250203T180930Z
LAST-MODIFIED:20250203T180930Z
UID:27784-1765584000-1765670399@statistics.sciences.ncsu.edu
SUMMARY:University Graduation
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/university-graduation-3/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251212
DTEND;VALUE=DATE:20251213
DTSTAMP:20260421T030926
CREATED:20251208T161509Z
LAST-MODIFIED:20251208T161541Z
UID:29001-1765497600-1765583999@statistics.sciences.ncsu.edu
SUMMARY:Department Graduation
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/department-graduation/
CATEGORIES:Department,Faculty,Graduate,Undergraduate
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251210
DTEND;VALUE=DATE:20251211
DTSTAMP:20260421T030926
CREATED:20251208T161351Z
LAST-MODIFIED:20251208T161351Z
UID:28998-1765324800-1765411199@statistics.sciences.ncsu.edu
SUMMARY:Final Exams
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/final-exams-71/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251209
DTEND;VALUE=DATE:20251210
DTSTAMP:20260421T030926
CREATED:20251208T161304Z
LAST-MODIFIED:20251208T161304Z
UID:28996-1765238400-1765324799@statistics.sciences.ncsu.edu
SUMMARY:Final Exams
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/final-exams-70/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251208
DTEND;VALUE=DATE:20251209
DTSTAMP:20260421T030926
CREATED:20251208T161218Z
LAST-MODIFIED:20251208T161218Z
UID:28994-1765152000-1765238399@statistics.sciences.ncsu.edu
SUMMARY:Final Exams
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/final-exams-69/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251203
DTEND;VALUE=DATE:20251204
DTSTAMP:20260421T030926
CREATED:20250203T180606Z
LAST-MODIFIED:20250203T180606Z
UID:27779-1764720000-1764806399@statistics.sciences.ncsu.edu
SUMMARY:Reading Day
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/reading-day-13/
CATEGORIES:College of Sciences Calendar,Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251128
DTEND;VALUE=DATE:20251129
DTSTAMP:20260421T030926
CREATED:20250203T173035Z
LAST-MODIFIED:20250203T173035Z
UID:27740-1764288000-1764374399@statistics.sciences.ncsu.edu
SUMMARY:University Closed - Holiday
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/university-closed-holiday-41/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251127
DTEND;VALUE=DATE:20251128
DTSTAMP:20260421T030926
CREATED:20250203T172924Z
LAST-MODIFIED:20250203T172924Z
UID:27738-1764201600-1764287999@statistics.sciences.ncsu.edu
SUMMARY:University Closed - Holiday
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/university-closed-holiday-40/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251126
DTEND;VALUE=DATE:20251127
DTSTAMP:20260421T030926
CREATED:20250203T180502Z
LAST-MODIFIED:20250203T180502Z
UID:27777-1764115200-1764201599@statistics.sciences.ncsu.edu
SUMMARY:No Classes - Holiday
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/no-classes-holiday-11/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251117T170000
DTEND;TZID=America/New_York:20251117T183000
DTSTAMP:20260421T030926
CREATED:20251106T143441Z
LAST-MODIFIED:20251106T143441Z
UID:28903-1763398800-1763404200@statistics.sciences.ncsu.edu
SUMMARY:November Professional Development Workshop
DESCRIPTION:Professional Development Workshop – November Session\nExploring Graduate School in Statistics: Programs + Student Panel\n📅 Date: Monday\, November 17\, 2025⏰ Time: 5:00 – 6:30 PM📍 Location: SAS Hall 2229💻 Virtual Option: https://go.ncsu.edu/stat.pdw✅ Hybrid Event — Attend in person or join via Zoom \n\nJoin us for this month’s Professional Development Workshop\, co-sponsored by the Department of Statistics and the Statistics Club. This special session will feature two parts: \n1. Information Session: Statistics Graduate Programs\nHear from Dr. Stallrich and Dr. Jeng\, who will provide an overview of the graduate programs they oversee. They will share key details about program structure\, opportunities\, and what students can expect in their graduate studies. \n2. Grad Student Panel: The Transition from Undergrad to Graduate School\nFollowing the information session\, we’ll host a panel conversation with current graduate students who will discuss their experiences moving from undergraduate study into graduate programs.The Q&A will be moderated\, with time for open questions from attendees. \nThis is a great event for anyone considering graduate studies in Statistics or wanting to learn more about life as a graduate student. \nWe hope you’ll join us—either in person or online!
URL:https://statistics.sciences.ncsu.edu/event/november-professional-development-workshop/
LOCATION:2229 SAS Hall
CATEGORIES:College of Sciences Calendar,Department,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251114T120000
DTEND;TZID=America/New_York:20251114T160000
DTSTAMP:20260421T030927
CREATED:20251028T175031Z
LAST-MODIFIED:20251028T175537Z
UID:28887-1763121600-1763136000@statistics.sciences.ncsu.edu
SUMMARY:Professional Development Workshop
DESCRIPTION:Databricks + NCSU Student Exchange \n\nJoin us on Nov 14th at 12pm in SAS Hall 1216 for some pizza and an afternoon with Databricks. \nDatabricks will be in Raleigh providing an exchange of information and ideas on how the market is being driven by an increased usage of  Data and AI.  We invite students interested in building a career in data\, analytics\, machine learning\, AI or otherwise\, to join the forum: \n– to better understand the market growth \n– how to build a competitive curriculum for potential employment\, \n– what is available from the school \n– what is free and available to use. \nAdditionally\, we will discuss the “Art of the Possible” with Data and AI with how that is changing the world for us as data consumers and data providers.  We encourage students to actively engage in an interactive Q&A with the Databricks team for the best understanding possible. \n\n++++\n\n\n\n\nTime\nActivity \n\n\n12:00 – 1pm\nLunch and Networking\n\n\n  \n1 – 1:45 pm\n  \nDatabricks Overview & Data and AI Market Potential\n\n\n  \n2 – 2:30 pm\n  \nWhat makes a good Data and AI employee candidate?\n\n\n  \n2:30 – 3:15 pm\n  \nHow to structure a career with a Data and AI trajectory with live Q&A\n\n\n3:15 – 4pm\n  \n  \n  \nWhat is available now for the students \n  \n\nEnrolling in Free Edition\nWhat is available in the FSU catalog and how can that be extended via the UA and Databricks Catalog\nReviewing the Catalog
URL:https://statistics.sciences.ncsu.edu/event/professional-development-workshop-3/
LOCATION:1216 SAS Hall
CATEGORIES:College of Sciences Calendar,Department,Graduate,Undergraduate
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251114T100000
DTEND;TZID=America/New_York:20251114T120000
DTSTAMP:20260421T030927
CREATED:20251020T175034Z
LAST-MODIFIED:20251020T175034Z
UID:28870-1763114400-1763121600@statistics.sciences.ncsu.edu
SUMMARY:Statistics Student Poster Session
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/statistics-student-poster-session/
LOCATION:5104 Commons
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251107T110000
DTEND;TZID=America/New_York:20251107T120000
DTSTAMP:20260421T030927
CREATED:20251020T174839Z
LAST-MODIFIED:20251031T142221Z
UID:28868-1762513200-1762516800@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 2203 SAS Hall\, NC State Main Campus \nTitle: Cracking the Code of the Unknown Virosphere: Detecting the Undetectable. \nPresenter:  Bonnie Hurwitz (BRC-NCSU) \nAbstract: Viruses are the most abundant biological entities on Earth and infect all major clades of life\, including bacteria\, archaea\, and eukaryotes. During infection\, viruses can influence host biology\, evolution\, and ecosystem dynamics; however\, 90% of viral DNA in natural communities is unknown\, limiting our understanding of host\, bacterial\, and viral interactions in diverse ecosystems. To explore these novel viral communities\, we are developing new tools and methods using machine learning and natural language processing to examine and interrelate viral and microbial communities with their environmental contexts in unprecedented depth and detail. Here\, I describe work in my lab to develop new capabilities in (i) viral sequence detection using composition-based pattern detection and machine learning\, and (ii) emerging biological stories in how we are using these tools to find novel viruses in the gut microbiome. These new approaches provide exciting avenues to find novel viruses and explore their interactions with diverse hosts and ecosystems without being limited to the known fraction of the viral population.
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-43/
LOCATION:2203 SAS Hall
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251031T110000
DTEND;TZID=America/New_York:20251031T120000
DTSTAMP:20260421T030927
CREATED:20251020T173731Z
LAST-MODIFIED:20251020T174546Z
UID:28864-1761908400-1761912000@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 2203 SAS Hall\, NC State Main Campus \nTitle: Synthetic Populations\, Personas and Agents \nPresenter: Georgiy Bobashev\, Ph.D. \nAbstract: Many experiments and estimate are not feasible or unethical to conduct with real people but possible in silico with synthetic individuals. I will present the construction\, and the use of geospatially explicit and statistically accurate person and household data\, which allow researchers to study community-and neighborhood-level effects\, design and test hypotheses that would not be possible without synthetic data. I will present the workflow for generating spatially explicit household- and individual-level synthetic populations for the United States representing 330 million individuals. Synthetic population could be used to probabilistically link multiple datasets for a specific purpose. There are statistical challenges with calibration and validation of these datasets. Agent-based models use these synthetic populations to study policy implications\, disease spread\, drug using behaviors\, etc. The use of synthetics individuals is now broadly expanding into health\, economic\, defense and other areas. With the developments of AI\, AI agents are taking over certain functions\, which creates new challenges in the development\, calibration and validation of these synthetic individuals.
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-42/
LOCATION:2203 SAS Hall
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251024T110000
DTEND;TZID=America/New_York:20251024T120000
DTSTAMP:20260421T030927
CREATED:20251013T174330Z
LAST-MODIFIED:20251013T174330Z
UID:28836-1761303600-1761307200@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 2203 SAS Hall\, NC State Main Campus\n\nSpeaker: Dr. Vadim Zipunnikov\, Johns Hopkins Bloomberg School of Public Health\n\nTitle: Developing more sensitive endpoints by leveraging novel statistical methods for Digital\nHealth Technologies (DHTs) data\n\nAbstract: Digital Health Technologies (DHT) are now used to continuously track physical\nactivity and sleep in many clinical studies. This DHT data provides tremendous opportunities to\ndevelop novel more sensitive clinical trial endpoints. There is\, however\, a large gap between the\ncomplexity of DHT data and statistical methodology for fully leveraging the potential of DHT.\nThis talk will discuss recent developments of novel DHT-centric statistical methods that can\nprovide more sensitive endpoints by extracting and fusing together information from temporal\,\ndistributional\, and time-series aspects of DHT data.\n\nBio: Dr. Vadim Zipunnikov is an Associate Professor of Biostatistics at Johns Hopkins Bloomberg School of Public Health. He co-leads the Wearable and Implantable Technology (WIT) group at Johns Hopkins University and serves as the Biostatistics Director of the NIMH-funded Motor Activity Research Consortium for Health (mMARCH)\, overseeing collection and analysis of large-scale digital health data across several clinical sites globally. Dr. Zipunnikov’s research focuses on developing advanced statistical methods for analyzing multimodal digital health data (DHT) from wearables and smartphones\, including accelerometry\, heart rate\, glucose monitors\, and ecological momentary assessment (EMA). In his work\, Dr. Zipunnikov collaborates with the Food and Drug Administration on digital analytics for drug development\, having developed a two-year course for the FDA’s Office of Biostatistics to equip drug reviewers with essential skills in DHT analytics. His research has garnered press coverage in NIH Research Matters\, NIH Directors’ blog\, TIME\, Washington Post\, Wall Street Journal\, CNN\, and BBC Radio. He has mentored 14 PhD students and 4 postdoctoral fellows and has authored over 100 peer-reviewed publications on digital biomarkers of physical/motor activity\, sleep\, and circadian rhythmicity in neurological and mental health disorders\, including Alzheimer’s Disease\, Multiple Sclerosis\, and Bipolar Disorder. He is known for his collaborative approach\, mentoring emerging researchers\, developing novel DHT-centric methods\, and his smile.
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-41/
LOCATION:2203 SAS Hall
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251013
DTEND;VALUE=DATE:20251014
DTSTAMP:20260421T030927
CREATED:20250203T180349Z
LAST-MODIFIED:20250203T181954Z
UID:27774-1760313600-1760399999@statistics.sciences.ncsu.edu
SUMMARY:No Classes - Fall Break
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/no-classes-fall-break-3/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251013
DTEND;VALUE=DATE:20251014
DTSTAMP:20260421T030927
CREATED:20250203T180349Z
LAST-MODIFIED:20250203T181954Z
UID:27774-1760313600-1760399999@statistics.sciences.ncsu.edu
SUMMARY:No Classes - Fall Break
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/no-classes-fall-break-3/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251010T110000
DTEND;TZID=America/New_York:20251010T120000
DTSTAMP:20260421T030927
CREATED:20250922T183145Z
LAST-MODIFIED:20250922T183145Z
UID:28632-1760094000-1760097600@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 2203 SAS Hall\, NC State Main Campus \nSpeaker:  Josh Startmer\,  Founder of StatQuest \nTitle: \nStatQuest: Origins plus musings on the intersection of Statistics and Machine Learning. \nABSTRACT: Although closely related\, subtle but important differences separate machine learning practitioners from statisticians. In this talk\, we will use statistical linear models to highlight these differences. Then\, we will show how to overcome them by combining linear models with regularization\, a machine learning method. The end result gives us the best of both statistics and machine learning in the form of a model that allows us to investigate mechanisms while being amenable to big datasets.
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-38/
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251003T110000
DTEND;TZID=America/New_York:20251003T120000
DTSTAMP:20260421T030927
CREATED:20250922T182300Z
LAST-MODIFIED:20250922T182705Z
UID:28629-1759489200-1759492800@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 2203 SAS Hall\, NC State Main Campus \nSpeaker: Elynn Chen \nAssistant Professor of Technology\, Operations and Statistics (TOPS) at NYU Stern School of Business \nTitle: Transfer Q-Learning: Stationary and Non-Stationary MDPs \nAbstract:\nIn dynamic decision-making scenarios across business\, healthcare\, and education\, leveraging data from diverse populations can significantly enhance reinforcement learning (RL) performance for specific target populations\, especially when target samples are limited. We develop comprehensive frameworks for transfer learning in RL\, addressing both stationary Markov decision processes (MDPs) with iterative Q-learning and non-stationary finite-horizon MDPs with backward inductive learning. \nFor stationary MDPs\, we propose an iterative Q-learning algorithm with knowledge transfer\, establishing theoretical justifications through faster convergence rates under similarity assumptions. For time-inhomogeneous finite-horizon MDPs\, we introduce two key innovations: (1) a novel “re-weighted targeting procedure” that enables vertical information-cascading along multiple temporal steps\, and (2) transfer deep Q-learning that leverages neural networks as function approximators. We demonstrate that while naive sample pooling strategies may succeed in regression settings\, they fail in MDPs\, necessitating our more sophisticated approach. We establish theoretical guarantees for both settings\, revealing the relationship between statistical performance and MDP task discrepancy. Our analysis illuminates how source and target sample sizes impact transfer effectiveness. The framework accommodates both transferable and non-transferable transition density ratios while assuming reward function transferability. Our analytical techniques have broader implications\, extending to supervised transfer learning with neural networks and domain shift scenarios. Empirical evidence from both synthetic and real datasets validates our theoretical results\, demonstrating significant improvements over single-task learning rates and highlighting the practical value of strategically constructed transferable RL samples in both stationary and non-stationary contexts.
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-37/
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250926T110000
DTEND;TZID=America/New_York:20250926T120000
DTSTAMP:20260421T030927
CREATED:20250918T141659Z
LAST-MODIFIED:20250924T164424Z
UID:28569-1758884400-1758888000@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 2203 SAS Hall\, NC State Main Campus \nTitle: Navigating the Complexities of Statistical Software Development \nSpeaker: Ryan Lekivetz\nDirector of Advanced Analytics\nR&D – JMP Statistical Discovery \nAbstract: Statistical software development is a multidisciplinary endeavor that spans product design\, implementation\, testing\, documentation\, and customer support. In this talk\, I’ll share personal experiences from working at JMP Statistical Discovery LLC\, highlighting the unique challenges and rewards of collaborating with professionals across industries and roles. From translating statistical ideas into usable features to supporting diverse users\, this presentation offers a behind-the-scenes look at the development process. It also introduces lesser-known career paths for statisticians and provides guidance for students and early-career professionals interested in applying their skills in software development.
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-36/
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250916
DTEND;VALUE=DATE:20250917
DTSTAMP:20260421T030927
CREATED:20250203T180221Z
LAST-MODIFIED:20250203T180221Z
UID:27772-1757980800-1758067199@statistics.sciences.ncsu.edu
SUMMARY:No Classes - Wellness Day
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/no-classes-wellness-day-6/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250912T110000
DTEND;TZID=America/New_York:20250912T120000
DTSTAMP:20260421T030927
CREATED:20250922T184413Z
LAST-MODIFIED:20250922T184413Z
UID:28638-1757674800-1757678400@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 2203 SAS Hall\, NC State Main Campus \nSpeaker: Sarah Lotspeich\nAssistant Professor\nDepartment of Statistical Sciences\nWake Forest University \nTitle: Bridging statistics and bioinformatics to overcome data challenges in the learning health system\n \nAbstract:  Data from Electronic health records (EHR) present a huge opportunity to operationalize a standardized whole-person health score in the learning health system and identify at-risk patients on a large scale\, except they are prone to missingness and errors. Ignoring these data quality issues could lead to biased statistical results and incorrect clinical decisions. Validation of EHR data (e.g.\, through chart reviews) can provide better-quality data. Still\, realistically\, only a subset of patients’ data can be validated\, and most protocols do not recover missing data. Using a representative sample of 1000 patients from the EHR at an extensive learning health system (100 of whom could be validated)\, we bridge statistics and bioinformatics methods to design\, conduct\, and analyze statistically efficient and robust studies of the ALI and healthcare utilization. Employing semiparametric sieve maximum likelihood estimation\, we robustly incorporate all available patient information into statistical models. Using targeted design strategies\, we examine ways to select the most informative patients for validation. Incorporating clinical expertise\, we devise a novel validation protocol to promote EHR data quality and completeness. Targeted validation with an enriched protocol allowed us to ensure the quality and promote the completeness of the EHR. Findings from our validation study were incorporated into statistical models\, which indicated that worse whole-person health was associated with higher odds of engaging in the healthcare system\, adjusting for age.
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-39/
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250901
DTEND;VALUE=DATE:20250902
DTSTAMP:20260421T030927
CREATED:20250203T172758Z
LAST-MODIFIED:20250203T172758Z
UID:27736-1756684800-1756771199@statistics.sciences.ncsu.edu
SUMMARY:University Closed - Holiday
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/university-closed-holiday-39/
CATEGORIES:Department,Faculty,Graduate,Undergraduate,University
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250829T110000
DTEND;TZID=America/New_York:20250829T120000
DTSTAMP:20260421T030927
CREATED:20250922T185021Z
LAST-MODIFIED:20250922T185021Z
UID:28640-1756465200-1756468800@statistics.sciences.ncsu.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Location: 2203 SAS Hall\, NC State Main Campus \nSpeaker: Kevin Gross\nProfessor\nStatistics Department\nNorth Carolina State University \n\nTitle: Towards a social theory of statistics: Some reflections on the beginning\, middle\, and end of my career\n \nAbstract:  When we talk about how science works\, we usually focus on the so-called scientific method\, stressing observation\, data analysis\, and hypothesis testing. What we don’t talk about much is how science works as a social process. What are the norms and institutions that govern scientific activity? What incentives do they create for individual researchers? How do those incentives shape the questions that scientists ask and the approaches they take? How do scientists work collectively to develop ever-improving models of the physical and natural world? How is scientific consensus formed—and what is it in the first place?  How does statistical theory facilitate or impede scientific discovery?  What role should it play?  All of these questions and more fall under the umbrella of an emerging field that some call the “science of science”. In this talk\, I will discuss one view of the science of science\, present a case study concerning the longevity of peer review\, and brazenly opine about how the science of science might intersect usefully with the discipline of statistics.    
URL:https://statistics.sciences.ncsu.edu/event/statistics-seminar-40/
CATEGORIES:College of Sciences Calendar,Department,Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250818
DTEND;VALUE=DATE:20250819
DTSTAMP:20260421T030927
CREATED:20250203T180116Z
LAST-MODIFIED:20250203T180116Z
UID:27770-1755475200-1755561599@statistics.sciences.ncsu.edu
SUMMARY:First Day of Classes
DESCRIPTION:
URL:https://statistics.sciences.ncsu.edu/event/first-day-of-classes-14/
CATEGORIES:Department,Faculty,Undergraduate,University
END:VEVENT
END:VCALENDAR