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X-WR-CALNAME:UNC Statistics & Operational Research
X-ORIGINAL-URL:https://stor.unc.edu
X-WR-CALDESC:Events for UNC Statistics & Operational Research
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TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
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TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
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TZID:UTC
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TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20210101T000000
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210507T120500
DTEND;TZID=America/New_York:20210507T130000
DTSTAMP:20220128T112032
CREATED:20210125T092825Z
LAST-MODIFIED:20210125T092825Z
UID:9969-1620389100-1620392400@stor.unc.edu
SUMMARY:Friday Lunch
DESCRIPTION:Informal lunch for the Fall 2020 graduate student cohort. Get to know each other better! Guests will include various faculty and graduate students from previous cohorts. Check your email for the Zoom link.
URL:https://stor.unc.edu/event/friday-lunch/2021-05-07/
LOCATION:Hanes Hall\, Hanes Hall\, Chapel Hill\, NC\, 27599\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210625T130000
DTEND;TZID=UTC:20210625T143000
DTSTAMP:20220128T112032
CREATED:20210623T170804Z
LAST-MODIFIED:20210623T170804Z
UID:12653-1624626000-1624631400@stor.unc.edu
SUMMARY:PHD Defense: Xi Yang
DESCRIPTION:Xi Yang \nMachine Learning Methods in HDLSS Settings \nDuring the exploration of high dimension-low-sample-size (HDLSS) data in different fields such as genetics\, finance\, computer science\, etc\, various machine learning methods have been developed. This dissertation includes the invention of novel methods and the improvement of current methods\, which are evaluated using cancer genetics data. \nThe statistical significance of the difference between subgroups is a central question in the setting of HDLSS data. The Direction Projection Permutation (DiProPerm) hypothesis test provides an answer to this that is directly connected to a visual analysis of the data. However\, under some circumstances\, the DiProPerm test can be less powerful and accurate when measuring the significance of the test pairs. In this dissertation\, we first introduce a new permutation method. This increases the power of the test in high signal situations. Furthermore\, the simulated null test statistics tend to be more reasonable and uni-modal. Then\, our theoretical analysis provides an adjustment to the inference for both permutation schemes. This enables us to exploit the improved power available. We also add confidence measures that reflect the Monte Carlo uncertainty in that test\, which is seen to be very useful for the comparison of results across different contexts. \nAnother important goal of this dissertation is to understand the drivers of Angle-Based Joint and Individual Variation Explained (AJIVE). An important open problem is a statistical inference on the AJIVE loadings to determine which are significant features of the analysis. Jackstraw is a method that generally aims to find the statistically significant drivers associated with unobserved latent variables. In this dissertation\, we develop a method based on similar ideas in the richer context of AJIVE to give a precise estimation. \nGenetic data sets are used to evaluate the above-proposed machine learning methods\, which also give results of independent interest to biologists. \n \n
URL:https://stor.unc.edu/event/phd-defense-xi-yang/
LOCATION:zoom
CATEGORIES:PhD Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210701T130000
DTEND;TZID=UTC:20210701T143000
DTSTAMP:20220128T112032
CREATED:20210623T170950Z
LAST-MODIFIED:20210623T170950Z
UID:12655-1625144400-1625149800@stor.unc.edu
SUMMARY:PHD Defense: Mark He
DESCRIPTION:Mark He \nCommunity Detection in Multimodal Networks \nCommunity detection on networks is a basic\, yet powerful and ever-expanding set of methodologies that is useful in a variety of settings. This dissertation discusses a range of different community detection on networks with multiple and non-standard modalities. A major focus of analysis is on the study of networks spanning several layers\, which represent relationships such as interactions over time\, different facets of high-dimensional data. These networks may be represented by several different ways; namely the few-layer (i.e. longitudinal) case as well as the many-layer (time-series cases). In the first case\, we develop a novel application of variational expectation maximization as an example of the top-down mode of simultaneous community detection and parameter estimation. In the second case\, we use a bottom-up strategy of iterative nodal discovery for these longer time-series\, abetted with the assumption of their structural properties. In addition\, we explore significantly self-looping networks\, whose features are inseparable from the inherent construction of spatial networks whose weights are reflective of distance information. These types of networks are used to model and demarcate geographical regions. We also describe some theoretical properties and applications of a method for finding communities in bipartite networks that are weighted by correlations between samples. We discuss different strategies for community detection in each of these different types of networks\, as well as their implications for the broader contributions to the literature. In addition to the methodologies\, we also highlight the types of data wherein these “non-standard” network structures arise and how they are fitting for the applications of the proposed methodologies: particularly spatial networks and multilayer networks. We apply the top-down and bottom-up community detection algorithms to data in the domains of demography\, human mobility\, genomics\, climate science\, psychiatry\, politics\, and neuroimaging. The expansiveness and diversity of these data speak to the flexibility and ubiquity of our proposed methods to all forms of relational data.
URL:https://stor.unc.edu/event/phd-defense-mark-he/
LOCATION:zoom
CATEGORIES:PhD Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210706T120000
DTEND;TZID=UTC:20210706T133000
DTSTAMP:20220128T112032
CREATED:20210628T181632Z
LAST-MODIFIED:20210628T181632Z
UID:12666-1625572800-1625578200@stor.unc.edu
SUMMARY:PhD Defense: Jack Prothero
DESCRIPTION:Modern data collection in bioinformatics and other big-data paradigms often incorporates traits derived from multiple different points of view of the observations. We call this data multi-view data or multi-block data. The field of data integration develops and applies new methods for studying multi-block data and identifying how different data blocks relate and differ. One major frontier in contemporary data integration research is methodology that can identify partially-shared structure between sub-collections of data blocks. This thesis presents our method for locating partially-shared structure among multi-block data: Data Integration Via Analysis of Subspaces (DIVAS). DIVAS combines new insights in angular subspace perturbation theory with recent developments in matrix signal processing and convex-concave optimization into one algorithm for parsing partially-shared structure. \n \nAn ever-present yet under-examined aspect of statistical analysis\, integrative or otherwise\, is data matrix centering. We find that additional forms of centering can produce novel modes of variation in functional data analysis (FDA) and data integration. We propose a unified framework and new terminology for centering operations. We clearly demonstrate the intuition behind and consequences of each centering choice with informative graphics. We also propose a new direction energy hypothesis test as part of a series of diagnostics for determining which choice of centering is best for a data set. \n \nBoth DIVAS and data matrix centering are illustrated throughout using multi-block data sets concerning cancer genomics and 20th century mortality. \n
URL:https://stor.unc.edu/event/phd-defense-jack-prothero/
LOCATION:zoom
CATEGORIES:PhD Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210707T110000
DTEND;TZID=UTC:20210707T130000
DTSTAMP:20220128T112032
CREATED:20210623T171332Z
LAST-MODIFIED:20210630T150105Z
UID:12657-1625655600-1625662800@stor.unc.edu
SUMMARY:PHD Defense: Hang Yu
DESCRIPTION:Sparse Machine Learning Methods for Prediction and Personalized Medicine \n With growing interest to use black-box machine learning for complex data with many feature variables\, it is critical to obtain a prediction model that only depends on a small set of features to maximize generalizability. Therefore\, feature selection remains to be an important and challenging problem in modern applications. Most of existing methods for feature selection are based on either parametric or semiparametric models\, so the resulting performance can severely suffer from model misspecification when high-order nonlinear interactions among the features are present. Thus\, nonparametric feature selection for high-dimensional data is an important and challenging problem in statistics and machine learning fields. We propose a new framework to perform nonparametric feature selection for both regression and classification problems. Under this framework\, we learn prediction functions through empirical risk minimization over a reproducing kernel Hilbert space (RKHS). The space is generated by a novel tensor product kernel which depends on a set of parameters that determine the importance of the features. We study the theoretical property of the kernel feature space and prove oracle selection property and Fisher consistency of our proposed method. Then we continue to apply the nonparametric feature selection framework for treatment decision making with high-dimensional data in personalized medicine field. With modification of the algorithms\, the computation process becomes fast and stable. We also include simulation studies and real word applications in the work to demonstrate the superior performance of the proposed method compared to exiting methods.
URL:https://stor.unc.edu/event/phd-defense-hang-yu/
LOCATION:zoom
CATEGORIES:PhD Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210810T090000
DTEND;TZID=UTC:20210810T130000
DTSTAMP:20220128T112032
CREATED:20210804T133458Z
LAST-MODIFIED:20210804T133555Z
UID:12679-1628586000-1628600400@stor.unc.edu
SUMMARY:CWE Exam: 634/635
DESCRIPTION:
URL:https://stor.unc.edu/event/cwe-exam-634-635/
LOCATION:NC
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210811T090000
DTEND;TZID=UTC:20210811T130000
DTSTAMP:20220128T112032
CREATED:20210804T133535Z
LAST-MODIFIED:20210804T133535Z
UID:12681-1628672400-1628686800@stor.unc.edu
SUMMARY:CWE Exam: 654/655
DESCRIPTION:
URL:https://stor.unc.edu/event/cwe-exam-654-655/
LOCATION:NC
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210812T090000
DTEND;TZID=UTC:20210812T130000
DTSTAMP:20220128T112032
CREATED:20210804T133627Z
LAST-MODIFIED:20210804T133627Z
UID:12683-1628758800-1628773200@stor.unc.edu
SUMMARY:CWE Exam: 641/642
DESCRIPTION:
URL:https://stor.unc.edu/event/cwe-exam-641-642/
LOCATION:NC
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210813T090000
DTEND;TZID=UTC:20210813T130000
DTSTAMP:20220128T112032
CREATED:20210804T133657Z
LAST-MODIFIED:20210804T133657Z
UID:12685-1628845200-1628859600@stor.unc.edu
SUMMARY:CWE Exam: 664/665
DESCRIPTION:
URL:https://stor.unc.edu/event/cwe-exam-664-665/
LOCATION:NC
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210816T100000
DTEND;TZID=UTC:20210816T120000
DTSTAMP:20220128T112032
CREATED:20210804T133839Z
LAST-MODIFIED:20210804T133839Z
UID:12687-1629108000-1629115200@stor.unc.edu
SUMMARY:Graduate Student Orientation
DESCRIPTION:The department will hold an orientation session for all new graduate students. This will be an in-person event in Hanes Hall.
URL:https://stor.unc.edu/event/graduate-student-orientation-2/
LOCATION:NC
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210827T153000
DTEND;TZID=UTC:20210827T163000
DTSTAMP:20220128T112032
CREATED:20210824T165241Z
LAST-MODIFIED:20210920T145543Z
UID:12763-1630078200-1630081800@stor.unc.edu
SUMMARY:Graduate Seminar: Kentaro Hoffman\, Elyse Borgert
DESCRIPTION:Kentaro Hoffman\n \nWhat’s In a Resolution: A Dempster-Schafer approach to Multinomial Hypothesis Tests \nResolution is an omnipresent part of statistical analysis. It comes up in introductory statistics when we are asked if we should treat time as a continuous variable or discrete variable. It comes up when we decide to forecast covid cases with continuous or discrete time models. It even shapes if we view a problem as appropriate for parametric or nonparametric tools. In this talk\, we would like to demonstrate how developments in Dempster-Shafer/Fiducial Inference can allow us to make informed decisions about correct resolution choice for Multinomial tests of uniformity. \nElyse Borgert \nPersistent topology of protein space \nProtein fold classification is a classic problem in structural biology and bioinformatics. We approach this problem using persistent homology. In particular\, we use alpha shape filtrations to compare a topological representation of the data with a different representation that makes use of knot-theoretic ideas. We use the statistical method of Angle-based Joint and Individual Variation Explained (AJIVE) to understand similarities and differences between these representations.
URL:https://stor.unc.edu/event/graduate-seminar-kentaro-hoffman-elyse-borgert/
LOCATION:120 Hanes Hall\, Hanes Hall\, Chapel Hill\, NC\, 27599\, United States
CATEGORIES:Graduate Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210901T160000
DTEND;TZID=UTC:20210901T180000
DTSTAMP:20220128T112032
CREATED:20210602T164512Z
LAST-MODIFIED:20210826T180254Z
UID:12423-1630512000-1630519200@stor.unc.edu
SUMMARY:Data Science & Analytics Resume Workshop
DESCRIPTION:This workshop will focus on marketing your skills and experience through resume writing in preparation for Data Science and Analytics-related roles. Hear from a career coach to learn about the major do’s and don’ts of building a solid resume and an attractive professional brand! \nhttps://app.joinhandshake.com/events/813834
URL:https://stor.unc.edu/event/data-science-analytics-resume-workshop/
LOCATION:NC
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210903T153000
DTEND;TZID=UTC:20210903T163000
DTSTAMP:20220128T112032
CREATED:20210901T201959Z
LAST-MODIFIED:20210901T201959Z
UID:12786-1630683000-1630686600@stor.unc.edu
SUMMARY:Graduate Seminar: Panagiotis Andreou
DESCRIPTION:Panagiotis Andreou\nUNC-Chapel Hill \nBayesian Bootstrap for the Transition Matrix of Finite State-Space Markov Chains \nWe study the problem of estimating the transition probability matrix on the context of a discrete-time finite state-space Markov chain\, based on an observed path of the chain. We briefly describe the asymptotic\, as well as the frequentist bootstrap approach to the problem. Bayesian bootstrap is then introduced\, first for independent and then for data from a Markov Chain. The Bayesian bootstrap estimator satisfies desired properties\, such as the Lindeberg Central Limit Theorem. Finally\, we provide a short simulation analysis to compare the Bayesian bootstrap with the asymptotic estimator
URL:https://stor.unc.edu/event/graduate-seminar-panagiotis-andreou/
LOCATION:NC
CATEGORIES:Graduate Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210908T160000
DTEND;TZID=UTC:20210908T180000
DTSTAMP:20220128T112032
CREATED:20210602T164608Z
LAST-MODIFIED:20210826T180406Z
UID:12425-1631116800-1631124000@stor.unc.edu
SUMMARY:Interview Preparation for Data Science & Analytics Related jobs
DESCRIPTION: \nThis panel will focus on preparing you for Data Science and Analytics-related roles. As you continue to build your skills and put them to work\, let UCS assist. Hear from select employers who will share helpful tips\, best practices\, interview process details\, and much more! \nCompany Panelists: Advance Auto Parts\, Centene Corporation\, Credit-Suisse\, Deluxe Corporation\, Prometheus Group\, and Red Ventures \nhttps://app.joinhandshake.com/events/813758
URL:https://stor.unc.edu/event/interview-preparation-for-data-science/
LOCATION:NC
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210910T153000
DTEND;TZID=UTC:20210910T163000
DTSTAMP:20220128T112032
CREATED:20210907T173520Z
LAST-MODIFIED:20210920T145533Z
UID:12806-1631287800-1631291400@stor.unc.edu
SUMMARY:Graduate Seminar: Kelly Bodwin & Joe Hammond
DESCRIPTION:Kelly Bodwin\nStatistics Department\nCalifornia Polytechnic State University\n(3:30- 4pm) \nData Science Education / Q&A \n\nJoe Hammond\n2nd Order Solutions\n(4-4:30 pm) \nCareer opportunities at 2nd Order Solutions
URL:https://stor.unc.edu/event/graduate-seminar-kelly-bodwin-joe-hammond/
LOCATION:120 Hanes Hall\, Hanes Hall\, Chapel Hill\, NC\, 27599\, United States
CATEGORIES:Graduate Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210915T130000
DTEND;TZID=UTC:20210915T170000
DTSTAMP:20220128T112032
CREATED:20210602T164108Z
LAST-MODIFIED:20210826T180600Z
UID:12418-1631710800-1631725200@stor.unc.edu
SUMMARY:Data Science and Analytics Career Fair
DESCRIPTION: \nUniversity Career Services and the Department of Statistics and Operations Research are hosting a joint career fair to bring together employers primarily seeking to fill Summer Internship and Full-Time Job roles with students from Data Science and STEM-related majors\, in addition to students pursuing Data Science minors. This event is open to all UNC majors\, but is targeted towards Data Science and STEM related majors\, in addition to students pursuing the new Data Science minor. \nhttps://app.joinhandshake.com/career_fairs/25005/student_preview
URL:https://stor.unc.edu/event/data-science-and-analytics-career-fair/
LOCATION:NC
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210917T153000
DTEND;TZID=UTC:20210917T163000
DTSTAMP:20220128T112032
CREATED:20210917T173523Z
LAST-MODIFIED:20210920T145522Z
UID:12835-1631892600-1631896200@stor.unc.edu
SUMMARY:Graduate Seminar: Pavlos Zoubouloglou
DESCRIPTION:Pavlos Zoubouloglou\nUNC-Chapel Hill \nDimension Reduction on Manifolds with an Emphasis on the Torus \nRecent advancements in the way information is collected have brought along the need to analyze data of a non-Euclidean nature. Dimension reduction has proved to be a challenging task for data that live on high dimensional directional manifolds (e.g. spheres and torii)\, as PCA disregards their inherent periodicity. For the first part of this talk\, an overview is provided of methods that have been developed to perform dimension reduction for data that lie on general directional manifolds\, including tangent plane-based methods and geodesic-based methods. In the context of spherical data\, Principal Nested Spheres provides a complete analogue of PCA and demonstrates that manifold-specific methods can outperform generic methods. For the second part of the talk\, we review methods that pertain to dimension reduction on torii. A novel approach to perform dimension reduction for toroidal data\, referred to as Scaled Torus Principal Component Analysis (STPCA)\, is introduced. Two data applications in molecular biology and astronomy show that STPCA outperforms existing methods for the investigated datasets.
URL:https://stor.unc.edu/event/graduate-seminar-pavlos-zoubouloglou/
LOCATION:120 Hanes Hall\, Hanes Hall\, Chapel Hill\, NC\, 27599\, United States
CATEGORIES:Graduate Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210920T153000
DTEND;TZID=UTC:20210920T163000
DTSTAMP:20220128T112032
CREATED:20210824T165509Z
LAST-MODIFIED:20210920T145552Z
UID:12765-1632151800-1632155400@stor.unc.edu
SUMMARY:STOR Colloquium: Iain Carmichael\, University of Washington-Seattle
DESCRIPTION:Iain Carmichael\nUniversity of Washington \nThe folded concave Laplacian spectral penalty learns block diagonal sparsity patterns with the strong oracle property \nStructured sparsity is an important part of the modern statistical toolkit. We say a set of model parameters has block diagonal sparsity up to permutations if its elements can be viewed as the edges of a graph that has multiple connected components. For example\, a block diagonal correlation matrix with K blocks of variables corresponds to a graph with K connected components whose nodes are the variables and whose edges are the correlations. This type of sparsity captures clusters of model parameters. To learn block diagonal sparsity patterns we develop folded concave Laplacian spectral penalty and provide a majorization-minimization algorithm for the resulting non-convex problem. We show this algorithm has the appealing computational and statistical guarantee of converging to the oracle estimator after two steps with high probability\, even in high-dimensional settings. The theory is then demonstrated in several classical problems including covariance estimation\, linear regression\, and logistic regression.
URL:https://stor.unc.edu/event/stor-colloquium-iain-carmichael-univ-of-washington/
LOCATION:120 Hanes Hall\, Hanes Hall\, Chapel Hill\, NC\, 27599\, United States
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210924T153000
DTEND;TZID=UTC:20210924T163000
DTSTAMP:20220128T112032
CREATED:20210928T013256Z
LAST-MODIFIED:20210928T013303Z
UID:12884-1632497400-1632501000@stor.unc.edu
SUMMARY:BIOS/STOR Student Seminar: Ann Marie Weideman & Kentaro Hoffman
DESCRIPTION:Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes (McCarthy et al.\, 2020)\nCardelino is a computational method for inferring the clonal tree configuration and clone of origin of individual cells assayed using single-cell RNA-seq (scRNA-seq). I will present this work from a clinical and Bayesian perspective and discuss how the Cardelino methodology applies to my own dissertation research. \nAnn Marie Weideman\n4th Year PhD Student in BIOS\nAdvisors: Joe Ibrahim and Yuchao Jiang (Dissertation) & Michael Hudgens (GRA) \nCausal Estimation of Seizure-Like Brain Activity\nSeizures and seizure-like brain activity are common in critically ill patients. Multiple studies have shown an association between high burden of seizures and seizure-like brain activity (‘Ictal- Interictal patterns’\, IIC patterns) and worsened clinical outcomes. However\, without better control of confounding factors such as anti-seizure medication administration and individual pharmacodynamic characteristics\, it is currently unknown if IIC patterns cause adverse outcomes or is merely a side effect of critical illness. Causal inference methods are a promising way to address this question from observational data. We use a matchingmethod\,`MatchingAfterLearningtoStretch'(MALTS)\, to control for confounders including demographic variables\, medical history\, and pharmacodynamics parameters. \nKentaro Hoffman\n5th Year PhD Student in STOR\nAdvisors:Kai Zhang and Cynthia Rudin
URL:https://stor.unc.edu/event/bios-stor-student-seminar-ann-marie-weideman-kentaro-hoffman/
LOCATION:133 Rosenau Hall\, Rosenau Hall\, Chapel Hill\, NC\, 27516\, United States
CATEGORIES:BIOS/STOR Student Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210929T160000
DTEND;TZID=UTC:20210929T170000
DTSTAMP:20220128T112032
CREATED:20210927T170910Z
LAST-MODIFIED:20210927T171145Z
UID:12868-1632931200-1632934800@stor.unc.edu
SUMMARY:Graduate Seminar: Scott Smith\, Advance Auto Parts
DESCRIPTION:Scott Smith\nData Science at Advance Auto Parts \nJoin alum Scott Smith (MS\, 2018) to learn about working as a data scientist at Advance Auto Parts.
URL:https://stor.unc.edu/event/graduate-seminar-scott-smith-advance-auto-parts/
LOCATION:120 Hanes Hall\, Hanes Hall\, Chapel Hill\, NC\, 27599\, United States
CATEGORIES:Graduate Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20211018T153000
DTEND;TZID=UTC:20211018T163000
DTSTAMP:20220128T112032
CREATED:20210930T191540Z
LAST-MODIFIED:20210930T191540Z
UID:12925-1634571000-1634574600@stor.unc.edu
SUMMARY:STOR Colloquium: Guorong Wu\, University of North Carolina Chapel Hill School of Medicine
DESCRIPTION:Guorong Wu\nUniversity of North Carolina School of Medicine \nUnderstanding the Selective Mechanism of Network Vulnerability in Alzheimer’s Disease Using Computational Neuroscience Approaches \nAbstract: We are now in the era of big data. The unprecedented amount of biomedical data allows us to answer the biological questions today that we couldn’t answer before. As a computer scientist\, this is the most exciting time in my entire career. In the last ten years\, I have been collaborating with neurology\, neuroscience\, genetics\, and imaging experts to understand the pathophysiological mechanism of Alzheimer’s disease (AD) and how AD-related genes affect aging brains. Specifically\, my lab is interested in establishing a neurobiological basis to quantify the structural/functional/behavior difference across individuals and discover reliable and putative biomarkers that will allow us to come up with personalized therapy and treatment for individuals. In this talk\, I would like to share my experience of integrating the domain knowledge of neuroscience into the development of computational tools for automated image analysis\, image interpretation\, and outcome prediction\, with the focus on imaging biomarkers and the computer-assisted early diagnostic engine for AD. At the end of this talk\, I will demonstrate the preliminary results of a recent research project where we aim to understand the selective mechanism of network vulnerability and resilience in AD using state-of-the-art network analysis approaches across neuroimaging and genetics data.
URL:https://stor.unc.edu/event/stor-colloquium-guorong-wu-university-of-north-carolina-chapel-hill-school-of-medicine/
LOCATION:zoom
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20211018T180000
DTEND;TZID=UTC:20211018T190000
DTSTAMP:20220128T112032
CREATED:20211005T180344Z
LAST-MODIFIED:20211005T180344Z
UID:12928-1634580000-1634583600@stor.unc.edu
SUMMARY:STAN Undergraduate Advising Session
DESCRIPTION:Join department advisors Mario Giacomazzo and Will Lassiter for a Q&A Session in which they will answer your questions regarding STAN classes\, the major and the minor. If you can’t make it to the Q&A Session\, the advisors will hold drop-in hours in the days following the event. See your email for details and the Zoom link! \n
URL:https://stor.unc.edu/event/stan-undergraduate-advising-session/
LOCATION:zoom
CATEGORIES:Advising and Information Sessions
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20211019T120000
DTEND;TZID=UTC:20211019T150000
DTSTAMP:20220128T112032
CREATED:20211005T180623Z
LAST-MODIFIED:20211005T180623Z
UID:12930-1634644800-1634655600@stor.unc.edu
SUMMARY:Drop-In Undergraduate Advising
DESCRIPTION:STAN students can meet with a department advisor. Links sent via student listserv.
URL:https://stor.unc.edu/event/drop-in-undergraduate-advising/
LOCATION:zoom
CATEGORIES:Advising and Information Sessions
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20211020T160000
DTEND;TZID=UTC:20211020T170000
DTSTAMP:20220128T112032
CREATED:20211020T122546Z
LAST-MODIFIED:20211020T122546Z
UID:12948-1634745600-1634749200@stor.unc.edu
SUMMARY:Grad Student Seminar: Kevin O'Connor
DESCRIPTION:Optimal transport for stationary Markov chains with an application to the comparison and alignment of weighted graphs \nInformally\, the optimal transport problem is to align\, or couple\, two distributions of interest as best as possible with respect to some prespecified cost. This problem has long attracted the interest of researchers and practitioners in a wide range of fields. Recent algorithmic advances and new applications have led to a boom in popularity for transport-based techniques in the statistics and machine learning communities. Ideas from optimal transport have contributed to novel approaches in clustering\, classification\, estimation\, modeling\, and other tasks. In this talk\, I will give an introduction to optimal transport\, focusing on computational techniques and applications in statistics and machine learning. After doing this\, I will discuss our work adapting the optimal transport problem to Markov chains. In particular\, we introduce a new problem referred to as the optimal transition coupling problem that incorporates the Markov structure of the marginal chains directly. Intuitively\, this new problem aims to synchronize the two Markov chains of interest as best as possible with respect to a cost specified by the user. Finally\, I will describe how the optimal transition coupling problem may be used to compare and align weighted graphs. We demonstrate that this new approach to graph optimal transport is sensitive to differences in both local and global structure and improves upon existing approaches in a graph classification task. \nThis is based on joint work with Andrew Nobel\, Kevin McGoff\, and Bongsoo Yi.
URL:https://stor.unc.edu/event/grad-student-seminar-kevin-oconnor/
LOCATION:120 Hanes Hall\, Hanes Hall\, Chapel Hill\, NC\, 27599\, United States
CATEGORIES:Graduate Seminar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20211029T153000
DTEND;TZID=UTC:20211029T163000
DTSTAMP:20220128T112032
CREATED:20211027T152509Z
LAST-MODIFIED:20211027T152509Z
UID:12963-1635521400-1635525000@stor.unc.edu
SUMMARY:BIOS/STOR Student Seminar: Sam Rosin\, BIOS and Alex Murph\, STOR
DESCRIPTION:Sam Rosin\nPhD Student in BIOS\nAdvisor: Michael Hudgens \nEstimating Seroprevalence of SARS-CoV-2\nDuring the COVID-19 pandemic\, governments have used\nseroprevalence studies to estimate the proportion of persons\nwithin a given population who have antibodies to SARS-CoV-2.\nHowever\, serologic assays are prone to false positives and\nnegatives\, and non-probability sampling methods may induce\nselection bias. Our work considers seroprevalence estimators\nthat address both challenges by leveraging validation and\ncovariate data. We study the estimators in simulations and apply\nthem to SARS-CoV-2 seroprevalence studies in North Carolina\nand Belgium from 2020. \nAlexander Murph \nPhD Student in STOR\nAdvisors: Jan Hannig and\nJonathan P Williams \nBayesian Change Point Detection for Mixed Data\nwith Missing Values\nMany models in-production assume that incoming data follows the same\ndistribution of the data on which the model was trained. In practice\, however\,\nthis distribution will often not remain static. We develop a change-point\ndetection system on data over time to locate major changes in the underlying\ndata distribution. This system has a strong practical motivation\, since it can be\nused to warn data scientists that an in-production model’s performance may\ndrop: a direct consequence of the training data no longer representing the\ncurrent data. This can drive decisions such as when to retrain a model or when\nto investigate discrepancies in the data.\nWe will use a real-world example (the Mayo Clinic’s Palliative Care model) to\nmotivate the need for this system. Our solution takes a hierarchical Bayesian\napproach that learns a vector of regimes as an unknown parameter. These\nregime vectors are assumed to come from a Hidden Markov Model (HMM);\ngiven a regime vector\, data within a regime are modeled using random graphs\nwith a varying edge set. Missing values and mixed variable types are handled\nusing a Bayesian latent variable approach; parameters are learned using a\nvariation of the Split-Merge algorithm and a Double Reversible Jump\nMetropolis-Hastings algorithm.
URL:https://stor.unc.edu/event/bios-stor-student-seminar-sam-rosin-bios-and-alex-murph-stor/
LOCATION:133 Rosenau Hall\, Rosenau Hall\, Chapel Hill\, NC\, 27516\, United States
CATEGORIES:BIOS/STOR Student Seminar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20211101T080000
DTEND;TZID=UTC:20211101T100000
DTSTAMP:20220128T112032
CREATED:20211020T125737Z
LAST-MODIFIED:20211027T151849Z
UID:12956-1635753600-1635760800@stor.unc.edu
SUMMARY:Ph.D. Defense: Aman Barot
DESCRIPTION:Techniques in network embedding and Gaussian comparison for high-dimensional statistics \nThis dissertation consists of research on two high-dimensional statistical problems. In the first part of the dissertation\, we study Gaussian comparison which is an important technique for comparing distributions and functionals of Gaussian random variables. We derive a Gaussian comparison result based on a smart-path argument. We show the significance of this result by an application to a problem of maximal correlations in high dimensions. \n \nIn the second part of the dissertation\, we focus on the problem of community detection on networks using node embedding methods. In recent decades\, network data sets containing millions and billions of nodes have become available. This has necessitated the development of scalable methods for their analysis. One such class of methods are methods for node embedding. Node embedding methods encode nodes of a network in a low-dimensional Euclidean space which allows one to use well-known methods for Euclidean spaces for network analysis. In this dissertation we study the problem of community detection using two well-known node embedding methods: DeepWalk and node2vec. We describe the network sparsity regimes when the k-means algorithm applied to the node embeddings detects communities for graphs generated from the stochastic block model\, and when such an approach might fail. We also describe how increasing the non-backtracking parameter in the node2vec method leads to provable improvements in community detection.
URL:https://stor.unc.edu/event/ph-d-defense-aman-barot/
LOCATION:zoom
CATEGORIES:PhD Defense
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BEGIN:VEVENT
DTSTART;TZID=UTC:20211101T153000
DTEND;TZID=UTC:20211101T163000
DTSTAMP:20220128T112032
CREATED:20210907T173640Z
LAST-MODIFIED:20211020T125304Z
UID:12808-1635780600-1635784200@stor.unc.edu
SUMMARY:STOR Colloquium: Thomas Lee\, University of California - Davis
DESCRIPTION:Thomas Lee\nUniversity of California\, Davis \nFiber Direction Estimation in Diffusion MRI \nDiffusion magnetic resonance imaging is a medical imaging technology to probe anatomical architectures of biological samples in an in vivo and non-invasive manner through measuring water diffusion. It is widely used to reconstruct white matter fiber tracts in brains. This can be done with the following steps. First\, one estimates the diffusion direction(s) for each voxel of the biological sample. As it is reasonable to assume that the diffusion directions from neighboring voxels are similar\, a local smoothing may be applied to the estimated tensors or directions to improve the estimation of diffusion directions. Finally\, a tracking algorithm is used to reconstruct fiber tracts based on (estimated) diffusion directions. \nMost commonly used tensor estimation methods assume a single tensor and do not work well when multiple principal diffusion directions are within a single voxel. The first part of this talk reports our work on identifying and estimating multiple diffusion directions within a voxel. The second part describes a direction smoothing method that greatly improves diffusion direction estimation in regions with crossing fibers. This smoothing method is shown to have excellent theoretical and empirical properties. Lastly\, this talk presents a fiber tracking algorithm that takes (estimated) diffusion directions as input and accommodates multiple directions within a voxel. \nThis is joint work with Seungyong Hwang\, Debashis Paul\, Jie Peng\, and Raymond Wong.
URL:https://stor.unc.edu/event/stor-colloquium-thomas-lee-university-of-california-davis/
LOCATION:NC
CATEGORIES:STOR Colloquium
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BEGIN:VEVENT
DTSTART;TZID=UTC:20211104T100000
DTEND;TZID=UTC:20211104T120000
DTSTAMP:20220128T112032
CREATED:20211020T125528Z
LAST-MODIFIED:20211028T152927Z
UID:12951-1636020000-1636027200@stor.unc.edu
SUMMARY:Ph.D. Defense: Kevin O'Connor
DESCRIPTION:Kevin O’Connor\nComputation and Consistent Estimation of Stationary Optimal Transport Plans \n In this dissertation\, we study optimal transport (OT) for stationary stochastic processes\, a field that we refer to as stationary optimal transport. Through example and theory\, we argue that when applying OT to stationary processes\, one should incorporate the stationarity into the problem directly\, constraining the set of allowed transport plans to those that are stationary themselves. In this way\, we only consider transport plans that respect the dependence structure of the marginal processes. We study this constrained OT problem from statistical and computational perspectives\, with an eye toward applications in machine learning and data science. In particular\, we \n\ndevelop algorithms for computing stationary OT plans of Markov chains.\nextend these tools for Markov OT to the alignment and comparison of weighted graphs.\npropose estimates of stationary OT plans based on finite sequences of observations.\n\nWe build upon existing techniques in OT as well as draw from a variety of fields including Markov decision processes\, graph theory\, and ergodic theory. In doing this\, we uncover new perspectives on OT and pave the way for additional applications and approaches in future work. \n
URL:https://stor.unc.edu/event/phd-defense-kevin-oconnor/
LOCATION:405 Dey Hall
CATEGORIES:PhD Defense
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BEGIN:VEVENT
DTSTART;TZID=UTC:20211105T153000
DTEND;TZID=UTC:20211105T163000
DTSTAMP:20220128T112032
CREATED:20211101T202641Z
LAST-MODIFIED:20211101T202641Z
UID:12973-1636126200-1636129800@stor.unc.edu
SUMMARY:Graduate Student Seminar: Prabhanka Deka
DESCRIPTION:Stochastic Fixed Point Equations arising from PageRank on\nStochastic Block Models \nStochastic Fixed Point Equations are equations of the form X = f(X)\, where X is a random variable and f is some function\, where the equality holds in distribution. We analyze the PageRank algorithm on a sparse\, directed Stochastic Block Models via coupling arguments\, where we couple the stochastic block model with a multi-type Galton-Watson tree and obtain a system of stochastic fixed point equations. \nThis is joint work with Mariana Olvera-Cravioto and Sayan Banerjee.
URL:https://stor.unc.edu/event/graduate-student-seminar-prabhanka-deka/
LOCATION:120 Hanes Hall\, Hanes Hall\, Chapel Hill\, NC\, 27599\, United States
CATEGORIES:Graduate Seminar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20211108T153000
DTEND;TZID=UTC:20211108T163000
DTSTAMP:20220128T112032
CREATED:20210824T165600Z
LAST-MODIFIED:20211019T171135Z
UID:12767-1636385400-1636389000@stor.unc.edu
SUMMARY:STOR Colloquium: Lauren Davis\, North Carolina A&T University
DESCRIPTION:Title: Improving equity and access in hunger relief supply chains: models for prediction and distribution of uncertain supply \nAbstract: \nDuring the past decade\, an increasing number of natural disasters and humanitarian emergencies have prompted significant research in the area of relief chain logistics and supply chain management. Much of the research has focused on challenges associated with stocking and distribution of relief supplies in response to sudden-onset disasters. However\, issues surrounding slow onset and persistent disasters (like food insecurity) present a unique set of challenges\, particularly with respect to the management and distribution of donated supply. Based on a partnership with a local non-profit hunger relief organization\, we describe the relief supply chain associated with the provision of food aid to populations suffering from hunger. We present predictive and descriptive models that quantify the availability of supply over time\, characterize demand\, and optimize the distribution of uncertain supply to ensure equity and improve access. Implications of our findings on operational efficiency and service delivery are discussed. \n \nBio: \nDr. Lauren Davis is a professor in the Department of Industrial & Systems Engineering at North Carolina Agricultural and Technical State University. She received her B.S. in Computational Mathematics from Rochester Institute of Technology; M.S. in Industrial Engineering from Rensselaer Polytechnic Institute\, and Ph.D. in Industrial Engineering from NC State University. Her research focuses on decision-making under uncertainty primarily using stochastic optimization techniques (Markov Decision Processes\, stochastic programming) and simulation. Her work has been applied to solve optimal stocking\, transportation scheduling and distribution decisions in for-profit and non-profit supply chains. She has more than 40 peer-reviewed journal papers and refereed conference proceedings addressing issues related to inventory management\, transportation scheduling\, port operations\, and emergency response in areas such as food supply chains\, food security\, port operations\, and humanitarian relief. Her work has been supported by the National Science Foundation\, Department of Homeland Security\, and US Department of Agriculture totaling more than $4 million in grant funding. Additionally\, her research examining hunger relief supply chains has been featured in CNN’s Great Big Story and NSF’s Discovery article series. She is currently the Principal Investigator for an NSF-funded National Research Traineeship grant that explores food security and hunger relief using computational data science.
URL:https://stor.unc.edu/event/stor-colloquium-lauren-davis-nc-at/
LOCATION:120 Hanes Hall\, Hanes Hall\, Chapel Hill\, NC\, 27599\, United States
CATEGORIES:STOR Colloquium
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