BEGIN:VCALENDAR PRODID:BedeWork V3.5 VERSION:2.0 METHOD:PUBLISH BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD BEGIN:STANDARD TZOFFSETFROM:-045602 TZOFFSETTO:-0500 TZNAME:EST DTSTART:18831118T120358 RDATE:18831118T120358 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:19180331T020000 RDATE:19180331T020000 RDATE:19190330T020000 RDATE:19200328T020000 RDATE:19210424T020000 RDATE:19220430T020000 RDATE:19230429T020000 RDATE:19240427T020000 RDATE:19250426T020000 RDATE:19260425T020000 RDATE:19270424T020000 RDATE:19280429T020000 RDATE:19290428T020000 RDATE:19300427T020000 RDATE:19310426T020000 RDATE:19320424T020000 RDATE:19330430T020000 RDATE:19340429T020000 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CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20220930T204149Z DESCRIPTION:This week's department seminar will feature talks from Statist ical Science undergraduate students involved in research. Each of the und ergraduate speakers will have 5-7 minutes to present their research\, fol lowed by a brief Q&A. A reception to celebrate our Statistical Science un dergraduate researchers and their advisors will follow. Come see the vari ety of work being done by undergraduate researchers in our department! DURATION:PT1H DTSTAMP:20230209T055713Z DTSTART;TZID=America/New_York:20230210T153000 LAST-MODIFIED:20230209T055713Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Undergrads Take Over StatSci Seminar! UID:CAL-8a0183a7-83184018-0183-902248a0-00001691demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=joan.durso@duke.edu:Joan Durso X-BEDEWORK-SPEAKER:Statistical Science Undergraduate Students X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Business CATEGORIES:Other CATEGORIES:Utilities CATEGORIES:Information Session CATEGORIES:Main CATEGORIES:Student CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230123T151610Z DESCRIPTION:Harvard Business School information session. Learn more about the MBA programs and application process. DURATION:PT1H30M DTSTAMP:20230126T101250Z DTSTART;TZID=America/New_York:20230301T190000 LAST-MODIFIED:20230126T101250Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Harvard Business School - Information Session (MBA and 2+2 Program ) UID:CAL-8a0290cd-85a68f68-0185-df33783d-00004d5ddemobedework@mysite.edu X-BEDEWORK-SUBMIT-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Information Session:/ public/aliases/Other/Information Session X-BEDEWORK-SUBMIT-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Business:/public/alia ses/Topics/Business X-BEDEWORK-CONTACT:Academic Deans\, Trinity College of Arts &\; Science s X-BEDEWORK-SUBMIT-COMMENT: X-BEDEWORK-SUBMITTER-EMAIL:ldb30@duke.edu X-BEDEWORK-DUKE-SPONSOR:/principals/users/agrp__ArtsandSciences_TrinityCol lege X-BEDEWORK-SUBMITTEDBY:ldb30 X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Information Session:/user/pu blic-user/Other/Information Session X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Business:/user/public-user/T opics/Business X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Student:/user/public-user/Ut ilities/Student X-BEDEWORK-STUDENT-CONTACT:Academic Deans\, Trinity College of Arts & Scie nces END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20220930T204149Z DESCRIPTION:To select outcomes for clinical trials testing experimental th erapies for Huntington disease\, a fatal neurodegenerative disorder\, ana lysts model how potential outcomes change over time. Yet\, subjects with Huntington disease are often observed at different levels of disease prog ression. To account for these differences\, analysts include time to clin ical diagnosis as a covariate when modeling potential outcomes\, but this covariate is often censored. One popular solution is imputation\, whereb y we impute censored values using predictions from a model of the censore d covariate given other data\, then analyze the imputed dataset. However\ , when this imputation model is misspecified\, our outcome model estimate s can be biased. To address this problem\, we developed a novel method\, dubbed ``ACE imputation.'' First\, we model imputed values as error-prone versions of the true covariate values. Then\, we correct for these error s using semiparametric theory. Specifically\, we derive an outcome model estimator that is consistent\, even when the censored covariate is impute d using a misspecified imputation model. Simulation results show that ACE imputation remains empirically unbiased even if the imputation model is misspecified\, unlike multiple imputation which yields $>100\\%$ bias. Ap plying our method to a Huntington disease study pinpoints outcomes for cl inical trials aimed at slowing disease progression. DURATION:PT1H DTSTAMP:20230222T183048Z DTSTART;TZID=America/New_York:20230310T153000 LAST-MODIFIED:20230222T183048Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Mission Imputable: Correcting for Berkson Error When Imputing a Ce nsored Covariate UID:CAL-8a0290b4-860465b2-0186-7a64719a-000053b5demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=statsci-seminar-coordina tor@duke.edu:Seminar Coordinator X-BEDEWORK-SPEAKER:Tanya P. Garcia\, Associate Professor of Biostatistics\ , UNC-Chapel Hill\, Gillings School of Public Health X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161313Z DESCRIPTION:Randomized experiments are the gold standard for inferring a c ausal effect. Consequently\, many organizations run thousands of randomiz ed experiments to quantify the impact of product changes\, which managers then use to inform deployment and investment decisions. Often\, these ex periments are conducted on customers arriving sequentially\; however\, th e analysis is only performed at the end of the study. This is undesirable because large effects can be detected before the end of the study\, whic h is especially important if the treatment effect is negative. Alternativ ely\, analysts could perform hypotheses tests more frequently and stop th e experiment when the estimated causal effect is statistically significan t\; this practice is often called ``peeking.'' Unfortunately\, peeking in validates the statistical guarantees and an increased type-1 error. Our p aper provides valid design-based confidence sequences\, sequences of conf idence intervals with uniform type-1 error guarantees over time for vario us sequential experiments in an assumption-light manner. In particular\, our results apply to the average treatment effect for different individua ls arriving sequentially\, the mean reward difference in multi-arm bandit settings with adaptive treatment assignments\, the contemporaneous treat ment effect for single time series experiment with carryover effects\, an d the average contemporaneous treatment effect in panel experiments. We f urther provide a variance reduction technique incorporating modeling assu mptions and covariates to reduce the confidence sequence width proportion al to how well we can predict the next outcome. Our work constructs both exact and asymptotic design-based confidence sequences\; however\, our ma in results focus on the asymptotic regime because of its general applicab ility and attractive properties. DURATION:PT1H DTSTAMP:20230331T183713Z DTSTART;TZID=America/New_York:20230331T153000 LAST-MODIFIED:20230331T183713Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Design-Based Anytime-Valid Causal Inference UID:CAL-8a0290b4-860465b2-0186-ffcbd333-00007db3demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:Iavor Bojinov\, Assistant Professor\, Business Administ ration\, Harvard Business School X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell END:VEVENT BEGIN:VEVENT CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:As a computational alternative to Markov chain Monte Carlo app roaches\, variational inference (VI) is becoming increasingly popular for approximating intractable posterior distributions in large-scale Bayesia n models due to its comparable efficacy and superior efficiency. Several recent works provide theoretical justifications of VI by proving its stat istical optimality for parameter estimation under various settings\; mean while\, formal analysis on the algorithmic convergence aspects of VI is s till largely lacking. In this talk\, we will discuss some recent advances towards studying convergence of the popular coordinate ascent variationa l inference algorithm. We will present some specific case studies and pro ceed to develop a general framework for studying such questions. DURATION:PT1H DTSTAMP:20230331T183741Z DTSTART;TZID=America/New_York:20230407T153000 LAST-MODIFIED:20230331T183741Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:On the Convergence of Coordinate Ascent Variational Inference UID:CAL-8a0290b4-860465b2-0186-ffce7eab-00007ed3demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:Anirban Bhattacharya\, Professor\, Department of Statis tics\, Texas A&M University X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell END:VEVENT BEGIN:VEVENT CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:A broad class of regression models that routinely appear in se veral fields of application can be expressed as partially or fully discre tized Gaussian linear regressions. Besides incorporating the classical Ga ussian response setting\, this class crucially encompasses probit\, multi nomial probit and tobit models\, among others\, and further includes popu lar extensions of such formulations to multivariate\, non-linear and dyna mic contexts. The relevance of these representations has motivated decade s of active research within the Bayesian field. A main reason for this co nstant interest is that\, unlike for the Gaussian response setting\, the posterior distributions induced by these models do not seem to belong to a known and tractable class\, under the commonly-assumed Gaussian priors. This has led to the development of several alternative solutions for pos terior inference relying either on sampling-based methods or on determini stic approximations\, that often experience scalability\, mixing and accu racy issues\, especially in high dimension. In this seminar\, I will revi ew\, unify and extend recent advances in Bayesian inference and computati on for such a class of models\, proving that unified skew-normal (SUN) di stributions (which include Gaussians as a special case) are conjugate to the general form of the likelihood induced by these formulations. This re sult opens new avenues for improved posterior inference\, under a broad c lass of widely-implemented models\, via novel closed-form expressions\, t ractable Monte Carlo methods based on i.i.d. samples from the exact SUN p osterior\, and more accurate and scalable approximations from variational Bayes and expectation-propagation. These results will be further extende d\, in asymptotic regimes\, to the whole class of Bayesian parametric mod els via novel limiting approximations relying on generalized skew-normal distributions. DURATION:PT1H DTSTAMP:20230407T190842Z DTSTART;TZID=America/New_York:20230414T153000 LAST-MODIFIED:20230407T190842Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:The role of skewed distributions in Bayesian inference: conjugacy\ , scalable approximations and asymptotics UID:CAL-8a0182b3-870a191e-0187-5d14a8db-00004af1demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Daniele Durante\, Assistant Professor\, Bocconi Univers ity X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:Discrete random probability measures stand out as effective to ols for Bayesian clustering. The investigation in the area has been very lively\, with a strong emphasis on nonparametric procedures based on eith er the Dirichlet process or on more flexible generalizations\, such as th e normalized random measures with independent increments (NRMI). The lite rature on finite-dimensional discrete priors is much more limited and mos tly confined to the standard Dirichlet-multinomial model. While such a sp ecification may be attractive due to conjugacy\, it suffers from consider able limitations when it comes to addressing clustering problems. In orde r to overcome these\, we introduce a novel class of priors that arise as the hierarchical compositions of finite-dimensional random discrete struc tures. Despite the analytical hurdles such a construction entails\, we ar e able to characterize the induced random partition and determine explici t expressions of the associated urn scheme and of the posterior distribut ion. A detailed comparison with (infinite-dimensional) NRMIs is also prov ided: indeed\, informative bounds for the discrepancy between the partiti on laws are obtained. Finally\, the performance of our proposal over exis ting methods is assessed on a real application where we study a publicly available dataset from the Italian education system comprising the scores of a mandatory nationwide test. DURATION:PT1H DTSTAMP:20230419T025126Z DTSTART;TZID=America/New_York:20230421T153000 LAST-MODIFIED:20230419T025126Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Finite-dimensional discrete random structures and Bayesian cluster ing UID:CAL-8a0182b3-870a191e-0187-95f55e32-00007e8bdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Tomasso Rigon\, Assistant Professor\, University of Mil ano Bicocca X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:Geographical and two-dimensional regression discontinuity desi gns (RDDs) extend the classic\, univariate RDD to multivariate\, spatial contexts. We propose a framework for analyzing such designs with Gaussian process regression. This yields a Bayesian posterior distribution of the treatment effect at every point along the border\, allowing for impact h eterogeneity. We can then aggregate along the border to obtain an overall local average treatment effect (LATE) estimate. We address nuances of ha ving a functional estimand defined on a border with potentially intricate topology\, particularly with respect to even defining the target estiman d of interest. The Bayesian estimate of the LATE can also be used as a te st statistic in a hypothesis test with good frequentist properties\, whic h we validate using simulations and placebo tests. We demonstrate our met hodology with a dataset of property sales in New York City\, to assess wh ether there is a discontinuity in housing prices at the border between sc hool district. We also discuss application of this method to the context of treatment as a function of two forcing variables\, such as falling bel ow a threshold for either a reading or math test.\n\nJoint with Lily An\, Zach Branson\, Maxime Rischard\, and Luke Bornn DURATION:PT1H DTSTAMP:20230420T185824Z DTSTART;TZID=America/New_York:20230428T153000 LAST-MODIFIED:20230420T185824Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:A Bayesian Nonparametric Approach to Geographic and Two-Dimensiona l Regression Discontinuity Designs UID:CAL-8a0182b3-870a191e-0187-9ff58db7-00003e69demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Luke Miratrix\, Associate Professor\, Harvard Universit y X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Other CATEGORIES:Utilities CATEGORIES:Meeting CATEGORIES:Main CONTACT;X-BEDEWORK-UID=18832e99-2a84ac8f-012a-85e0def0-00000038:Rauch\, Lo ri CREATED:20230605T143339Z DESCRIPTION:The Statistical Science Department encourages all to attend th e defense of this dissertation. DURATION:PT2H DTSTAMP:20230605T143339Z DTSTART;TZID=America/New_York:20230620T083000 LAST-MODIFIED:20230605T143339Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Ecological Modeling via Bayesian Nonparametric Species Sampling Pr iors UID:CAL-8a018cb3-8855248a-0188-8bfa595f-00000f4bdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Meeting:/user/public-user/Ot her/Meeting X-BEDEWORK-SPEAKER:Alessandro Zito X-BEDEWORK-SUBMITTEDBY:lr3 for Statistical Science (agrp_StatisticalScienc e) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:Bayesian deep Gaussian processes (DGPs) outperform ordinary GP s as surrogate models of complex computer experiments when response surfa ce dynamics are non-stationary\, which is especially prevalent in aerospa ce simulations. Yet DGP surrogates have not been deployed for the canoni cal downstream task in that setting: reliability analysis through contour location (CL). Level sets separating passable vs. failable operating co nditions are best learned through strategic sequential design. There are two limitations to modern CL methodology which hinder DGP integration in this setting. First\, derivative-based optimization underlying acquisit ion functions is thwarted by sampling-based Bayesian (i.e.\, MCMC) infere nce\, which is essential for DGP posterior integration. Second\, canonic al acquisition criteria\, such as entropy\, are famously myopic to the ex tent that optimization may even be undesirable. Here we tackle both of t hese limitations at once\, proposing a hybrid criteria that explores alon g the Pareto front of entropy and (predictive) uncertainty\, requiring ev aluation only at strategically located "triangulation" candidates. We sh owcase DGP CL performance in several synthetic benchmark exercises and on a real-world RAE-2822 transonic airfoil simulation. DURATION:PT1H DTSTAMP:20230829T135017Z DTSTART;TZID=America/New_York:20230901T153000 LAST-MODIFIED:20230829T135017Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Contour Location for Airfoil Simulation Experiments Using Deep Gau ssian Processes UID:CAL-8a02906b-8a0a73d8-018a-418f325f-00004b5ademobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=stat:Karen Whitesell X-BEDEWORK-SPEAKER:Annie Booth\, Assistant Professor\, North Carolina Stat e University X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:We study multiple testing in the normal means problem with est imated\nvariances that are shrunk through empirical Bayes methods. The si tuation is asymmetric in\nthat a prior is posited for the nuisance parame ters (variances) but not the primary\nparameters (means).\nIf the prior w ere known\, one could proceed by computing p-values\nconditional on sampl e variances\; a strategy called partially Bayes inference by Sir David\nC ox. These conditional p-values satisfy a Tweedie-type formula and are app roximated at\nnearly-parametric rates when the prior is estimated by nonp arametric maximum likelihood. If\nthe variances are in fact fixed\, the a pproach retains type-I error guarantees. As is common\nin the empirical B ayes paradigm\, our results hinge on the interpretation of the prior as t he\nfrequency distribution of the nuisance parameters\, and should be con trasted with e.g.\, the\nconditional predictive p-values of Bayarri and B erger.\n\nBased on joint work with Bodhisattva Sen. DURATION:PT1H DTSTAMP:20230905T124056Z DTSTART;TZID=America/New_York:20230908T153000 LAST-MODIFIED:20230905T124056Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Empirical partially Bayes multiple testing and compound χ² decisio ns UID:CAL-8a02906b-8a0a73d8-018a-655a1575-00004651demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=stat:Karen Whitesell X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-SPEAKER:Nikolaos (Nikos) Ignatiadis\, Assistant Professor\, The University of Chicago X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Sustainability CATEGORIES:Ethics CATEGORIES:Human Rights CATEGORIES:Civic Engagement/Social Action CATEGORIES:Global CATEGORIES:Other CATEGORIES:Utilities CATEGORIES:Information Session CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230830T160124Z DESCRIPTION:Join us for an overview of DukeEngage and the application proc ess\, plus a chance to hear from previous participants! DURATION:PT1H DTSTAMP:20230830T160201Z DTSTART;TZID=America/New_York:20230914T163000 LAST-MODIFIED:20230830T160201Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:DukeEngage Info Session UID:CAL-8a02906b-8a0a73d8-018a-472e27fe-00003993demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Information Session:/user/pu blic-user/Other/Information Session X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Global:/user/public-user/Top ic of Event Focused on a Country or Continent (if applicable)/Global X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Civic Engagement/Social Acti on:/user/public-user/Topics/Civic Engagement_Social Action X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Ethics:/user/public-user/Top ics/Ethics X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Human Rights:/user/public-us er/Topics/Human Rights X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Sustainability:/user/public- user/Topics/Sustainability X-BEDEWORK-CS;X-BEDEWORK-PARAM-DESCRIPTION="/principals/users/agrp_DukeEng age,":DukeEngage X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=aaron.crouse@duke.edu:Aa ron Crouse X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:1059 X-BEDEWORK-IMAGE-Y2:706 X-BEDEWORK-IMAGE-CROP-WIDTH:1059 X-BEDEWORK-IMAGE-CROP-HEIGHT:706 X-BEDEWORK-IMAGE-ALT-TEXT:DukeEngage Info Sessions X-BEDEWORK-SUBMITTEDBY:sr14 for Kenan Institute for Ethics (agrp_KenanInst itute) X-BEDEWORK-IMAGE:/public/Images/2023 DukeEngage Info Sessions DukeCal_2023 0830040124PM.png X-BEDEWORK-THUMB-IMAGE:/public/Images/2023 DukeEngage Info Sessions DukeCa l_20230830040124PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:This talk will delve into two major causal inference obstacles : (1) identifying which variables to account for and (2) assessing the im pact of unmeasured variables. The first half of the talk will showcase a Causal Quartet. In the spirit of Anscombe's Quartet\, this is a set of fo ur datasets with identical statistical properties\, yet different true ca usal effects due to differing data generating mechanisms. These simple da tasets provide a straightforward example for statisticians to point to wh en explaining these concepts to collaborators and students. The second ha lf of the talk will focus on how statistical techniques can be leveraged to examine the impact of a potential unmeasured confounder. We will exami ne sensitivity analyses under several scenarios with varying levels of in formation about potential unmeasured confounders\, introducing the tipr R package\, which provides tools for conducting sensitivity analyses in a flexible and accessible manner. DURATION:PT1H DTSTAMP:20230911T161935Z DTSTART;TZID=America/New_York:20230915T153000 LAST-MODIFIED:20230911T161935Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Causal Quartet: When statistics alone do not tell the full story UID:CAL-8a02906b-8a0a73d8-018a-84f81761-00005d37demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=stat:Karen Whitesell X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-SPEAKER:Lucy D'Agostino McGowan\, Assistant Professor\, Wake Fo rest University X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Other CATEGORIES:Utilities CATEGORIES:Information Session CATEGORIES:Main CATEGORIES:Student CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None X-BEDEWORK-COST:NA CREATED:20230824T154441Z DESCRIPTION:Harvard Business School - Information Session (Deferred MBA\, 2+2 Program). DURATION:PT1H30M DTSTAMP:20230825T132537Z DTSTART;TZID=America/New_York:20230920T190000 LAST-MODIFIED:20230825T132537Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Harvard Business School - Information Session (Deferred MBA\, 2+2 Program) UID:CAL-8a02906b-8a0a73d8-018a-28381ec9-00005740demobedework@mysite.edu X-BEDEWORK-SUBMIT-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Information Session:/ public/aliases/Other/Information Session X-BEDEWORK-CONTACT:Trinity Academic Deans X-BEDEWORK-SUBMIT-COMMENT: X-BEDEWORK-SUBMITTER-EMAIL:ldb30@duke.edu X-BEDEWORK-DUKE-SPONSOR:/principals/users/agrp__ArtsandSciences_TrinityCol lege X-BEDEWORK-SUBMITTEDBY:ldb30 X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Information Session:/user/pu blic-user/Other/Information Session X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Student:/user/public-user/Ut ilities/Student X-BEDEWORK-STUDENT-CONTACT:Trinity Academic Deans END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:Multivariate linear regression and randomization-based inferen ce are \ntwo essential methods in statistics and econometrics. Neverthele ss\,\nthe problem of producing a randomized test for the value of a singl e\nregression coefficient that is exactly valid when errors are exchangea ble\,\nand which is asymptotically valid for the best linear predictor\, has\nremained elusive. In this paper\, we produce a test that is exactly\ nvalid with exchangeable errors and which allows for general covariate\nd esigns\; covariates may be continuous as well as discrete\, and may be\nc orrelated. The test is asymptotically valid when the errors are not\nexch angeable\, in particular in the presence of conditional heteroskedasticit y. DURATION:PT1H DTSTAMP:20230919T153025Z DTSTART;TZID=America/New_York:20230922T153000 LAST-MODIFIED:20230919T153025Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:An Exact t-Test UID:CAL-8a03932d-8a96243a-018a-ae1067bc-00007bc6demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=stat:Karen Whitesell X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-SPEAKER:Guillaume Pouliot\, University of Chicago X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:Generalized linear mixed models are the workhorse of applied S tatistics. In modern applications\, from political science to electronic marketing\, it is common to have categorical factors with large number of levels. This arises naturally when considering interaction terms in surv ey-type data\, or in recommender-system type of applications. In such con texts it is important to have a scalable computational framework\, that i s one whose complexity scales linearly with the number of observations $n $ and parameters $p$ in the model. Popular implementations\, such as thos e in lmer\, although highly optimized they involve costs that scale polyn omially with $n$ and $p$. We adopt a Bayesian approach (although the esse nce of our arguments applies more generally) for inference in such contex ts and design families of variational approximations for approximate Baye sian inference with provable scalability. We also provide guarrantees for the resultant approximation error and in fact link that to the rate of c onvergence of the numerical schemes used to obtain the variational approx imation.\nThis is joint work with Giacomo Zanella (Bocconi) and Max Gople rud (Pittsburgh) DURATION:PT1H DTSTAMP:20230927T222713Z DTSTART;TZID=America/New_York:20230929T153000 LAST-MODIFIED:20230927T222713Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Accurate and scalable large-scale variational inference for mixed models UID:CAL-8a03932d-8a96243a-018a-cc5965ce-00001951demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Omiros Papaspiliopoulos\, Professor\, Bocconi Universit y X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:To sanitize data for the purpose of disclosure control is to d estroy its precision in some way. When done in an explicit or controlled manner\, the imprecision can be salvaged to the statistician's benefit. T his talk discusses how imprecision that results from privacy protection m ay be appropriated to improve our statistical understanding from the data at hand. Two ideas are sketched. The first demonstrates how knowledge ab out the imprecision can be harnessed to facilitate statistical computatio n and recover inference in a manner faithful to the downstream task. The second employs the imprecise probabilities vocabulary to establish analyt ical limits for key inferential quantities under minimal knowledge or ass umptions about the downstream task and the privacy mechanism. Both ideas serve as persuasive arguments for a formal and transparent approach to di sclosure control. \n\nThis body of work bears witness to the challenges t hat emerged from the U.S. Census Bureau's revamp of its disclosure avoida nce system for the 2020 Decennial Census\, and more broadly through effor ts to expand data access to support research and policymaking under moder n data governance directives. To that end\, I conclude with an assessment of strongly quantitative notions of privacy\, notably differential priva cy\, against prevailing qualitative guidelines of confidentiality protect ion to highlight its benefits and limitations. DURATION:PT1H DTSTAMP:20230927T222737Z DTSTART;TZID=America/New_York:20231006T153000 LAST-MODIFIED:20230927T222737Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:When a little imprecision can help: Case studies from statistical privacy UID:CAL-8a03932d-8a96243a-018a-d8ab0463-00006c13demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Ruobin Gong\, Assistant Professor\, Rutgers X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:The tremendous increase in computation capabilities of edge de vices\, along with the rapid market infiltration of powerful AI chips\, h as led to explosive interest in collaborative analytics\, such as federat ed learning\, that distribute model learning across diverse sources to pr ocess more of the user's data at the origin of creation. To date\, these efforts have focused mainly on predictive modeling\, where the goal is t o create a global or personalized predictive map (often a deep network) t hat leverages knowledge from different sources while circumventing the ne ed to share raw data. In this talk\, I argue that predictive modeling\, without untangling the nature of heterogeneity across users\, can lead to swift and evident failures. With this in mind\, I then present: i) A des criptive framework capable of extracting interpretable and identifiable f eatures that describe what is shared and unique across diverse data datas ets\, ii) A prescriptive framework that utilizes the learned features for collaborative sequential design wherein dispersed users effectively dist ribute their trial & error efforts to improve and fast-track the optimal design process. I conclude the talk by describing some of our real-world prototyping and testing efforts.\n \n \nBio: Raed Al Kontar is an as sistant professor in the Industrial & Operations Engineering department a t the University of Michigan and an affiliate with the Michigan Institute for Data Science. Raed's research focuses on collaborative\, distributed \, and decentralized data science. Raed obtained an undergraduate degree in civil & environmental engineering and mathematics from the American Un iversity of Beirut in 2014 and a master's degree in statistics in 2017 an d a Ph.D. degree in Industrial & System Engineering in 2018\, both from t he University of Wisconsin-Madison. Raed's research is currently supporte d by NSF\, including a 2022 CAREER award\, NIH\, NLM\, and various indust ry collaborators DURATION:PT1H DTSTAMP:20231012T163654Z DTSTART;TZID=America/New_York:20231020T153000 LAST-MODIFIED:20231012T163654Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Collaborative and Federated Data Analytics Beyond Predictive Model ing UID:CAL-8a03932d-8a96243a-018a-e6fe16e4-00006c5bdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Raed Al Kontar\, Assistant Professor\, University of Mi chigan X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:It is increasingly possible to develop treatments for psychiat ric disorders by making targeted interventions on the brain. However\, d esigning an appropriate protocol requires many choices. We propose a meth od that identifies electrical dynamics across brain regions related to il lness states or behaviors and employs these patterns to design interventi on protocols. Specifically\, the observed electrical activity of the bra in is statistically modeled as a superposition of activity from latent el ectrical functional connectome (electome) networks. The activity of these latent networks defines a brain state that predicts disease state\, beha vior\, or outcomes. These electome networks are explainable in their spec tral power and directional relationships between brain regions\, facilita ting the design of testable protocols on key relationships. We present a case study on social aggression\, where we identify an electome network a ssociated with aggressive behavior and develop a machine-learning control led protocol that selectively reduces aggression without affecting pro-so cial behavior. We conclude with ongoing efforts in causal discovery and mediation analysis to further understand and improve this system. DURATION:PT1H DTSTAMP:20231031T190331Z DTSTART;TZID=America/New_York:20231103T153000 LAST-MODIFIED:20231031T190331Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Machine Learning to Infer and Control Brain State UID:CAL-8a018d0d-8b5366bc-018b-871e9aa9-000023e5demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-SPEAKER:David Carlson\, Assistant Professor\, Civil and Environ mental Engineering\, Duke University X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20231106T143008Z DESCRIPTION:In this presentation\, we will introduce data science\, AI\, a nd biostatistics career opportunities in academic health care. The Duke B ERD (Biostatistics\, Epidemiology\, and Research Design) Methods Core is a team of staff and faculty with expertise in data science\, biostatistic s\, informatics\, and other quantitative areas who collaborate with biome dical researchers to solve important health-related problems across all a reas of medicine. Quantitative scientists in the BERD Core design studies \, implement and design methods and clinical trials\, develop real-time p rediction models\, and ensure that results are interpreted appropriately to improve health care. These scientists have exciting careers that enabl e them to provide high-quality analytics\, facilitate reproducible resear ch workflows\, and disseminate impactful results in interdisciplinary col laborative environments. One collaboration we will highlight is with the Center for AIDS Research (CFAR). We have paid internships to solve proble ms in the area of in HIV/AIDS research available for Summer 2024! DURATION:PT1H15M DTSTAMP:20231107T182137Z DTSTART;TZID=America/New_York:20231108T150500 LAST-MODIFIED:20231107T182137Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Career Opportunities in Academic Healthcare UID:CAL-8a018ccf-8b87f80e-018b-a50a768f-000008e9demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=megan.e.kelly@duke.edu:M egan Kelly Deyncourt X-BEDEWORK-SPEAKER:Gina-Maria Pomann PhD\, Assistant Professor\, Departmen t of Biostatistics and Bioinformatics\, Duke University School of Medicin e and Richard Barfield\, Staff Biostatistician\, Department of Biostatis tics and Bioinformatics\, Duke University School of Medicine X-BEDEWORK-DUKE-SERIES:Statistical Science Proseminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:1050 X-BEDEWORK-IMAGE-Y2:700 X-BEDEWORK-IMAGE-CROP-WIDTH:1050 X-BEDEWORK-IMAGE-CROP-HEIGHT:700 X-BEDEWORK-IMAGE-ALT-TEXT:portraits of speakers X-BEDEWORK-IMAGE:/public/Images/Screenshot 2023-11-07 at 1.19.28 PM_202311 07062137PM.png X-BEDEWORK-THUMB-IMAGE:/public/Images/Screenshot 2023-11-07 at 1.19.28 PM_ 20231107062137PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20231106T143320Z DESCRIPTION:Bayesian optimization (BayesOpt) optimizes time-consuming-to-e valuate objective functions arising in materials design\, drug discovery\ , neural architecture design\, and other applications. It combines a Baye sian posterior distribution over the objective function with a decision-t heoretic acquisition function that quantifies the value of objective func tion and constraint evaluations ("experiments").\n\nWhile BayesOpt is a b lack-box optimization approach\, we have recently shown that "peeking ins ide the box" can improve performance by several orders of magnitude. Key to this approach are statistical methods that incorporate additional inf ormation beyond the values of the objective function. For example\, when optimizing quality in a manufacturing process\, these methods incorporate observations of quality after each stage of the process\, not just the q uality of the final output.\n\nThis idea also offer a new way to interact with humans who have trouble choosing a single objective function. Rathe r than estimating a Pareto frontier like traditional multi-objective opti mization methods\, we can model the human as having a utility function dr awn from a Bayesian prior. By iteratively updating a posterior on the hu man's utility function in response to questions ("which tradeoff between cost and quality do you like better?") and using this knowledge to priori tize experiments\, we can identify a set of solutions whose maximum utili ty is likely to be large. This approach better leverages information abou t user preferences to provide much better efficiency than traditional mul t-objective methods.\n\nWe describe the ideas behind these approaches and how they are being used to design novel energy materials in collaboratio n and optimize online platforms. DURATION:PT1H DTSTAMP:20231106T143756Z DTSTART;TZID=America/New_York:20231110T153000 LAST-MODIFIED:20231106T143756Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Grey-Box Bayesian Optimization for Human-in-the-loop Optimization UID:CAL-8a018ccf-8b87f80e-018b-a50d657b-000008eademobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:Peter Frazier Eleanor and Howard Morgan Professor Opera tions Research and Information Engineering Cornell University X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=Karen.Whitesell@duke.edu :Karen Whitesell END:VEVENT BEGIN:VEVENT CATEGORIES:Africa focus CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=2c918084-613c4fe3-0161-43be89e7-00003081:Thakkar\, Rohini CREATED:20231031T190328Z DESCRIPTION:Rapid-fire military takeovers in Mali\, Burkina Faso\, and Nig er\; Wagner - the Kremlin's proxy force - moving into the region while th e French army is moving out amidst a groundswell of hostility against Fra nce's postcolonial presence\; and the fastest-growing Jihadist insurgency in the world... Of late\, the swath of arid land stretching across Afric a south of the Sahara has been much in the news. Five experts will engage in a timely conversation about the Sahel. \n\nLeif Brottem is Associate Professor of Global Development Studies at Grinnell College in the state of Iowa. \n\nMarc-Antoine Pérouse de Montclos\, a Doctor in political sc ience\, is a Senior Researcher at the Institut de recherche pour le dével oppement (IRD). \n\nAlioune Sow is a joint appointment in French and Afri can Studies at the University of Florida. \n\nStephen W. Smith\, Ph.D.\, teaches African Studies at Duke with a research focus on conflict analysi s\, demography/population age structure and Franco-African postcolonialit y.. DURATION:PT1H30M DTSTAMP:20231101T164812Z DTSTART;TZID=America/New_York:20231116T113000 LAST-MODIFIED:20231101T164812Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:The Sahel Region: Coups\, Jihadism\, Wagner & Anti-French Sentimen ts UID:CAL-8a018d0d-8b5366bc-018b-871e8cda-000023e4demobedework@mysite.edu URL:https://global.duke.edu/dai-events X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Africa focus:/user/public-us er/Topic of Event Focused on a Country or Continent (if applicable)/Afric a focus X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:Leif Brottem (Grinnell College)\, Marc-Antoine Pérouse de Montclos (IRD\, Paris)\, Alioune Sow (University of Florida)\, Stephen Smith (Duke) X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:661.5 X-BEDEWORK-IMAGE-Y2:441 X-BEDEWORK-IMAGE-CROP-WIDTH:661.5 X-BEDEWORK-IMAGE-CROP-HEIGHT:441 X-BEDEWORK-IMAGE-ALT-TEXT:Flyer of the event which has title\, day\, date\ , time of the event. Speakers&\;amp\;amp\;#39\; headshots\, names and affiliations\, Africa Initiative logo and image of a few soldiers. X-BEDEWORK-SUBMITTEDBY:rt54 for Africa Initiative (agrp_AfricaInitiative) X-BEDEWORK-IMAGE:/public/Images/v2 The Sahel Region Flyer_20231101044220PM .png X-BEDEWORK-THUMB-IMAGE:/public/Images/v2 The Sahel Region Flyer_2023110104 4220PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:Transportation of measure underlies many powerful tools for Ba yesian inference\, density estimation\, and generative modeling. The cent ral idea is to deterministically couple a probability measure of interest with a tractable "reference" measure (e.g.\, a standard Gaussian). Such couplings are induced by transport maps and enable direct simulation from the desired measure simply by evaluating the transport map at samples fr om the reference. \n\nWhile an enormous variety of representations and co nstructive algorithms for transport maps have been proposed in recent yea rs\, it is inevitably advantageous to exploit the potential for low-dimen sional structure in the associated probability measures. I will discuss t wo such notions of low-dimensional structure\, and their interplay with t ransport-driven methods for sampling and inference. The first seeks to ap proximate a high-dimensional target measure as a low-dimensional update o f a dominating reference measure. The second is low-rank conditional stru cture\, where the goal is to replace conditioning variables with low-dime nsional projections or summaries. In both cases\, under appropriate assum ptions on the reference or target measures\, one can derive gradient-base d upper bounds on the associated approximation error and minimize these b ounds to identify good subspaces for approximation. The associated subspa ces then dictate specific structural ansatzes for transport maps that rep resent the target of interest.\n\nI will showcase several algorithmic ins tantiations of this idea\, with examples drawn from Bayesian inverse prob lems\, data assimilation\, and/or simulation-based inference. DURATION:PT1H DTSTAMP:20231113T174258Z DTSTART;TZID=America/New_York:20231117T153000 LAST-MODIFIED:20231113T174258Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:On low-dimensional structure in transport and inference UID:CAL-8a018ccf-8b87f80e-018b-c9c787f2-00005366demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-SPEAKER:Youssef Marzouk\, Professor\, MIT X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:We study the problem of distribution-free dependence detection and modeling through the new framework of binary expansion statistics (B EStat). The binary expansion testing (BET) avoids the problem of non-unif orm consistency and improves upon a wide class of commonly used methods ( a) by achieving the minimax rate in sample size requirement for reliable power and (b) by providing clear interpretations of global relationships upon rejection of independence. The binary expansion approach also connec ts the symmetry statistics with the current computing system to facilitat e efficient bitwise implementation. Modeling with the binary expansion li near effect (BELIEF) is motivated by the fact that two linearly uncorrela ted binary variables must be also independent. Inferences from BELIEF are easily interpretable because they describe the association of binary var iables in the language of linear models\, yielding convenient theoretical insight and striking parallels with the Gaussian world. With BELIEF\, on e may study generalized linear models (GLM) through transparent linear mo dels\, providing insight into how modeling is affected by the choice of l ink. We explore these phenomena and provide a host of related theoretical results. This is joint work with Benjamin Brown and Xiao-Li Meng. DURATION:PT1H DTSTAMP:20231115T185834Z DTSTART;TZID=America/New_York:20231201T153000 LAST-MODIFIED:20231115T185834Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:BET and BELIEF UID:CAL-8a018ccf-8b87f80e-018b-d4597622-00003ff8demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-SPEAKER:Kai Zhang\, Associate Professor\, UNC X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230320T161608Z DESCRIPTION:In problems such as variable selection and graph estimation\, models are characterized by Boolean logical structure such as presence or absence of a variable or an edge. Consequently\, false positive and fals e negative errors can be specified as the number of variables or edges th at are incorrectly included/excluded in an estimated model. However\, the re are several other problems such as ranking\, clustering\, and causal i nference in which the associated model classes do not admit transparent n otions of false positive and false negative errors due to the lack of an underlying Boolean logical structure. In this paper\, we present a generi c approach to endow a collection of models with partial order structure\, which leads to a hierarchical organization of model classes as well as n atural analogs of false positive and false negative errors. We describe m odel selection procedures that provide false positive error control in ou r general setting and we illustrate their utility with numerical experime nts.\nThis is joint work with Peter Buehlmann and Venkat Chandrasekaran. DURATION:PT1H DTSTAMP:20231201T182334Z DTSTART;TZID=America/New_York:20231208T153000 LAST-MODIFIED:20231201T182334Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Model selection over partially ordered sets UID:CAL-8a018ccf-8b87f80e-018c-269f2d1b-0000757cdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-SPEAKER:Armeen Taeb\, Assistant Professor\, University of Washi ngton X-BEDEWORK-DUKE-SERIES:StatSci Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20231229T143339Z DESCRIPTION:The total electron content (TEC) maps can be used to estimate the signal delay of GPS due to the ionospheric electron content between a receiver and a satellite. This delay can result in a GPS positioning err or. Thus\, it is important to monitor and forecast the TEC maps. However\ , the observed TEC maps have big patches of missingness in the ocean and scattered small areas on the land. Thus\, precise imputation and predicti on of the TEC maps are crucial in space weather forecasting. \n\nIn this talk\, I first present several extensions of existing matrix completion a lgorithms to achieve TEC map reconstruction\, accounting for spatial smoo thness and temporal consistency while preserving essential structures of the TEC maps. We show that our proposed method achieves better reconstruc ted TEC maps as compared to existing methods in the literature. I will al so briefly describe the use of our large-scale complete TEC database. The n\, I present a new model for forecasting time series data distributed on a matrix-shaped spatial grid\, using the historical spatiotemporal data and auxiliary vector-valued time series data. Large sample asymptotics of the estimators for both finite and high dimensional settings are establi shed. Performances of the model are validated with extensive simulation s tudies and an application to forecast the global TEC distributions. DURATION:PT1H DTSTAMP:20240102T213722Z DTSTART;TZID=America/New_York:20240112T153000 LAST-MODIFIED:20240102T213722Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Video Imputation and Prediction Methods with Applications in Space Weather UID:CAL-8a018ccf-8b87f80e-018c-b5febd0f-00003128demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=Karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Yang Chen\, Assistant Professor\, University of Michiga n X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar Series X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240113T005531Z DESCRIPTION:The biochemical functions of proteins\, such as catalyzing a c hemical reaction or binding to a virus\, are typically conferred by the g eometry of only a handful of atoms. This arrangement of atoms\, known as a motif\, is structurally supported by the rest of the protein\, referre d to as a scaffold. A central task in protein design is to identify a di verse set of stabilizing scaffolds to support a motif known or theorized to confer function. This long-standing challenge is known as the motif-sc affolding problem.\n \nIn this talk\, I describe a statistical approach I have developed to address the motif-scaffolding problem. My approach in volves (1) estimating a distribution supported on realizable protein stru ctures and (2) sampling scaffolds from this distribution conditioned on a motif. For step (1) I adapt diffusion generative models to fit example protein structures from nature. For step (2) I develop sequential monte carlo algorithms to sample from the conditional distributions of these mo dels. I finally describe how\, with experimental and computational colla borators\, I have generalized and scaled this approach to generate and ex perimentally validate hundreds of proteins with various functional specif ications.\n \nBio:\nBrian Trippe is a postdoctoral fellow at Columbia Uni versity in the Department of Statistics\, and a visiting researcher at th e Institute for Protein Design at the University of Washington. He comple ted his Ph.D. in Computational and Systems Biology at the Massachusetts I nstitute of Technology where worked on Bayesian methods for inference in hierarchical linear models. In his research\, Brian develops statistical machine learning methods to address challenges in biotechnology and medic ine\, with a focus on generative modeling and inference algorithms for pr otein engineering. DURATION:PT1H DTSTAMP:20240116T171502Z DTSTART;TZID=America/New_York:20240119T153000 LAST-MODIFIED:20240116T171502Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Probabilistic methods for designing functional protein structures UID:CAL-8a008bcc-8cfbd545-018d-00511b7e-00005306demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Brian Trippe\, Columbia University X-BEDEWORK-DUKE-SERIES:Statistical Science X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240109T033728Z DESCRIPTION:Optimization techniques\, such as dual ascent\, alternating di rection method of multipliers\, and majorization-minimization\, are widel y used in high-dimensional applications. The strengths of optimization ar e the high computing efficiency and the ease of inducing point estimates on useful constrained spaces\, such as those satisfying low rank\, low ca rdinality or combinatorial structure. For uncertainty quantification arou nd point estimate\, a popular generalized Bayes solution known as Gibbs p osterior exponentiates the negative loss function\, and forms a posterior density. Despite successful theoretic justifications\, Gibbs posterior d istribution is supported in a high-dimensional space and\, hence often do es not inherit nice properties in computing efficiency and constraints fr om optimization. In this work\, we are motivated by a discovery that a la rge class of penalized profile likelihoods\, which partially maximize ove r a subset of parameters\, in fact enjoy equivalence to another generativ e model for the data. This leads us to explore a new generalized Bayes ap proach that views the likelihood as an equality-constrained function\, ba sed on data\, parameters\, and a conditionally deterministic latent varia ble equal to an optimization solution. This new likelihood can be justifi ed as a special case of augmented likelihood where the latent variable is typically exploited to model dependency among the data. Therefore\, this framework coined "bridged posterior'' conforms to the Bayesian methodolo gy. A surprising theoretical finding is that under mild conditions\, the square root n-adjusted bridged posterior distribution of the parameters c onverges to the same asymptotical normal that the canonical integrated po sterior converges to. Therefore\, our results formally dispel a long-time belief that partial optimization over latent variables might lead to an underestimation of parameter uncertainty. We demonstrate the practical ad vantages of our approach in applications\, such as classification with pa rtially labeled data and harmonization of multiple brain scan networks. DURATION:PT1H DTSTAMP:20240112T200140Z DTSTART;TZID=America/New_York:20240122T114500 LAST-MODIFIED:20240112T200140Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Bridged Posterior: Optimization\, Profile Likelihood and a New App roach of Generalized Bayes UID:CAL-8a018ccf-8b87f80e-018c-ec4beffe-000055e3demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Leo Duan\, Assistant Professor\, University of Florida X-BEDEWORK-DUKE-SERIES:Statistical Science X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240110T024331Z DESCRIPTION:The exploratory and interactive nature of modern data analysis often introduces selection bias\, posing challenges for traditional stat istical inference methods. A common strategy to address this bias is by c onditioning on the selection event. However\, this often results in a con ditional distribution that is intractable and requires Markov chain Monte Carlo (MCMC) sampling for inference. Notably\, some of the most widely u sed selection algorithms yield selection events that can be characterized as polyhedra\, such as the lasso for variable selection and the epsilon- greedy algorithm for multi-armed bandit problems. This talk will present a method that is tailored for conducting inference following polyhedral s election. The method transforms the variables constrained within a polyhe dron into variables within a unit cube\, allowing for exact sampling. Com pared to MCMC\, the proposed method offers superior speed and accuracy\, providing a practical and efficient approach for conditional selective in ference. Additionally\, it facilitates the computation of the selection-a djusted maximum likelihood estimator\, enabling MLE-based inference. Nume rical results demonstrate the enhanced performance of the proposed method compared to alternative approaches for selective inference. DURATION:PT1H DTSTAMP:20240110T024331Z DTSTART;TZID=America/New_York:20240126T153000 LAST-MODIFIED:20240110T024331Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:An Exact Sampler for Inference after Polyhedral Selection UID:CAL-8a018ccf-8b87f80e-018c-f140e857-0000167edemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-SPEAKER:Sifan Liu\, Stanford University X-BEDEWORK-DUKE-SERIES:Statistical Science X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240110T140507Z DESCRIPTION:Mixed effect modeling for longitudinal data is challenging whe n the observed data are random objects\, which are complex data taking va lues in a general metric space without either global linear or local line ar (Riemannian) structure. In such settings the classical additive error model and distributional assumptions are unattainable. Due to the rapid a dvancement of technology\, longitudinal data containing complex random ob jects\, such as covariance matrices\, data on Riemannian manifolds\, and probability distributions are becoming more common. Addressing this chall enge\, we develop a mixed-effects regression for data in geodesic spaces\ , where the underlying mean response trajectories are geodesics in the me tric space and the deviations of the observations from the model are quan tified by perturbation maps or transports. A key finding is that the geod esic trajectories assumption for the case of random objects is a natural extension of the linearity assumption in the standard Euclidean scenario to the case of general geodesic metric spaces. Geodesics can be recovered from noisy observations by exploiting a connection between the geodesic path and the path obtained by global Fréchet regression for random object s. The effect of baseline Euclidean covariates on the geodesic paths is m odeled by another Fréchet regression step. We study the asymptotic conver gence of the proposed estimates and provide illustrations through simulat ions and real-data applications. DURATION:PT1H DTSTAMP:20240127T192305Z DTSTART;TZID=America/New_York:20240129T114500 LAST-MODIFIED:20240127T192305Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:CANCELLED:Geodesic Mixed Effects Models for Repeatedly Observed/Lo ngitudinal Random Objects UID:CAL-8a018ccf-8b87f80e-018c-f3b0eca1-00002992demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Satarupa Bhattacharjee\, Pennsylvania State X-BEDEWORK-DUKE-SERIES:Statistical Science X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240110T022314Z DESCRIPTION:Monte Carlo methods span a range of disciplines\, drawing inte rest from statisticians\, computer scientists\, physicists\, among others . In the Monte Carlo workflow\, the upstream task involves designing and analyzing sampling algorithms\, while the downstream task focuses on effi ciently using these samples to form estimators. In this talk\, I will dis cuss recent developments of both aspects. The first part introduces the ' Spectral Telescope' framework for analyzing Gibbs samplers\, and discusse s its relationship with the spectral independence technique recently deve loped in theoretical computer science. The second part focuses on the dev elopment of unbiased estimators through the combination of Markov Chain M onte Carlo and Multilevel Monte Carlo methods\, highlighting their potent ial in parallel computing.\n\nBiosketch: Guanyang Wang is an Assistant Pr ofessor in the Department of Statistics at Rutgers University. He complet ed his Ph.D. in Mathematics with a Ph.D. minor in Statistics at Stanford University\, advised by Professor Persi Diaconis. Guanyang Wang's researc h primarily centers on Monte Carlo methods\, applied probability\, and st atistical computing. Recently\, he is also working on quantum computing. His research receives support from both the NSF and an Adobe Data Science Research Award. DURATION:PT1H DTSTAMP:20240110T165332Z DTSTART;TZID=America/New_York:20240202T153000 LAST-MODIFIED:20240110T165332Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:The Many Facets of Monte Carlo Methods: From Sampling Algorithms t o Unbiased Estimators UID:CAL-8a018ccf-8b87f80e-018c-f12e53cb-000015bddemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Guanyang Wang\, Assistant Professor\, Rutgers Universit y X-BEDEWORK-DUKE-SERIES:Statistical Science X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240110T184203Z DESCRIPTION:Recent interest has centered on uncertainty quantification for machine learning models. For the most part\, this work has assumed indep endence of the observations. However\, many of the most important problem s arising across scientific fields\, from genomics to climate science\, i nvolve systems where dependence cannot be ignored. In this talk\, I will investigate inference on machine learning models in the presence of depen dence. \n\nIn the first part of my talk\, I will consider a common practi ce in the field of genomics in which researchers compute a correlation ma trix between genes and threshold its elements in order to extract groups of independent genes. I will describe how to construct valid p-values ass ociated with these discovered groups that properly account for the group selection process. While this is related to the literature on selective inference developed in the past decade\, this work involves inference abo ut the covariance matrix rather than the mean\, and therefore requires an entirely new technical toolset. This same toolset can be applied to quan tify the uncertainty associated with canonical correlation analysis after feature screening. \n\nIn the second part of my talk\, I will turn to an important problem in the field of oceanography as it relates to climate science. Oceanographers have recently applied random forests to estimate carbon export production\, a key quantity of interest\, at a given locati on in the ocean\; they then wish to sum the estimates across the world's oceans to obtain an estimate of global export production. While quantifyi ng uncertainty associated with a single estimate is relatively straightfo rward\, quantifying uncertainty of the summed estimates is not\, due to t heir complex dependence structure. I will adapt the theory of V-statistic s to this dependent data setting in order to establish a central limit th eorem for the summed estimates\, which can be used to quantify the uncert ainty associated with global export production across the world's oceans. \n\nThis is joint work with my postdoctoral supervisors\, Daniela Witten (University of Washington) and Jacob Bien (University of Southern Califor nia). DURATION:PT1H DTSTAMP:20240126T191301Z DTSTART;TZID=America/New_York:20240205T114500 LAST-MODIFIED:20240126T191301Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Inference for machine learning under dependence UID:CAL-8a018ccf-8b87f80e-018c-f4ae7643-00007e00demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Arkajyoti Saha\, University of Washington X-BEDEWORK-DUKE-SERIES:Statistical Science X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240122T180117Z DESCRIPTION:This presentation offers insights into the work environment at JMP Statistical Discovery LLC\, a prominent statistical software company \, with a specific focus on the Research and Development (R&D) division. Within this dynamic industry\, I'll showcase unique aspects of working at JMP\, emphasizing the collaborative and innovative culture that defines the company. In this talk\, I will share insights on software testing\, a critical aspect of the company's operations. While the testing group at JMP have graduate degrees in statistics and related fields\, software tes ting is not a topic covered in most statistics programs. This talk will d iscuss the challenges and intricacies of software testing and cutting-edg e testing techniques used within JMP.\n \nDr. Ryan Lekivetz is a Senior M anager of Advanced Analytics R&D at JMP\, heading the Design of Experimen ts (DOE) and Reliability Development team. Ryan earned his doctorate in s tatistics from Simon Fraser University in Burnaby\, British Columbia. He has published papers on DOE topics in peer-reviewed journals and holds ma ny patents that he shares with his team members. His research interests i nclude design of experiments\, combinatorial testing\, and assessing the usability of statistical software through designed experiments. DURATION:PT1H15M DTSTAMP:20240126T210345Z DTSTART;TZID=America/New_York:20240207T150500 LAST-MODIFIED:20240126T210345Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:A Day in the Life: JMP R&D UID:CAL-8a0292fd-8d13410f-018d-32557553-00001ea8demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=megan.deyncourt@duke.edu :Megan Deyncourt X-BEDEWORK-SPEAKER:Dr. Ryan Lekivetz\, Senior Manager\, Advanced Analytics R&D\, JMP X-BEDEWORK-DUKE-SERIES:Statistical Science Proseminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240112T194732Z DESCRIPTION:In this talk\, I will discuss semi-parametric estimation when nuisance parameters cannot be estimated consistently\, focusing in partic ular on the estimation of average treatment effects\, conditional correla tions\, and linear effects under high-dimensional GLM specifications. In this challenging regime\, even standard doubly-robust estimators can be i nconsistent. I describe novel approaches which enjoy consistency guarante es for low-dimensional target parameters even though standard approaches fail. For some target parameters\, these guarantees can also be used for inference. Finally\, I will provide my perspective on the broader implica tions of this work for designing methods which are less sensitive to bias es from high-dimensional prediction models. DURATION:PT1H DTSTAMP:20240117T144245Z DTSTART;TZID=America/New_York:20240209T153000 LAST-MODIFIED:20240117T144245Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Debiasing in the inconsistency regime UID:CAL-8a008bcc-8cfbd545-018c-ff372232-00003c95demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Michael Celentano\, University of California\, Berkeley X-BEDEWORK-DUKE-SERIES:Statistical Science X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240216T212156Z DESCRIPTION:Ensemble decision tree methods such as XGBoost\, RF\, and BART have gained enormous popularity in data science for their superior perfo rmance in machine learning regression and classification tasks. In this p aper\, we develop a new Bayesian graph-split-based additive decision tree s method\, called GS-BART\, to improve the performance of Bayesian additi ve decision trees for complex dependent data with graph relations. The ne w method adopts a highly flexible split rule complying with graph structu re to relax the axis-parallel split rule assumption in most existing ense mble decision tree models. We design a scalable informed MCMC algorithm l everaging a gradient-based recursive algorithm on spanning trees or chain s to sample the graph-split-based decision tree. The superior performance of the method over conventional ensemble tree models and gaussian proces s models is illustrated in various regression and classification tasks fo r spatial and network data analysis. DURATION:PT1H DTSTAMP:20240216T220058Z DTSTART;TZID=America/New_York:20240223T153000 LAST-MODIFIED:20240216T220058Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:GS-BART: Graph split additive decision trees for classification an d nonparametric regression of spatial and network data UID:CAL-8a0292fd-8d13410f-018d-b3cc235e-000015c7demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Huiyan Sang Professor of Statistics Texas A&M Universit y X-BEDEWORK-DUKE-SERIES:Statistical Science X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240125T163537Z DESCRIPTION:following the launch of GPT4-Agent\, GPT4 has demonstrated its flexibility in utilizing tools like Advanced Data Analytics (ADA\, previ ously known as code interpreter) and DALL- E3\, although the details of G PT4-Agent have not been fully disclosed. Over the past years\, we have in tensively studied the core functionalities of GPT4\, progressively develo ping a system comparable to GPT4-Agent named InfiAgent. Initially\, we re plicated Codex and discovered that while existing models such as CodeLlam a\, StarCoder\, and WizardCoder excel in programming capabilities\, they fall short in handling FreeformQA problems for coding. To address this\, we created InfiCoder-the first open-source model capable of handling text -to-code\, code-to-code\, and freeform code-related QA tasks simultaneous ly. Building on this\, we developed InfiCoder-Eval (FreeformQA benchmark) \, which includes 270 high-quality automated test questions. Our findings indicate that even GPT4 has room for improvement in this area (achieving a score rate of only 59.13%). Based on InfiCoder\, we launched the InfiA gent framework. This framework first defines the problem framework and ev aluation objectives for data analysis. Then\, in line with the data analy sis scenarios\, we developed a specialized Agent system based on the Reac t format and LLM. This system integrates an LLM with programming capabili ties and a sandbox environment for executing Python code\, generating sol utions\, and corresponding code through multiple rounds of dialogues. It is the industry's first Agent framework closest to the capabilities of AD A. Additionally\, we expanded the application scenarios of InfiAgent\, es pecially in multimodal reasoning. We observed that there is significant r oom for improvement in the current GPT4V (achieving a score rate of only 74.44%). These achievements not only reveal the tremendous potential of I nfiAgent but also showcase our possible directions in surpassing the capa bilities of GPT4.\n \nBio: Dr. Hongxia Yang\, Ph.D.\, from Duke Universit y\, has published over 100 papers in top-tier conferences and journals an d holds more than 50 patents in the USA and China. Currently\, she serves as the Head of large models at ByteDance US. Previously\, she worked as a research staff member at IBM T.J. Watson Research Center\, as a princip al scientist for Computational Advertising at Yahoo!\, as an AI scientist and director at Alibaba DAMO Academy\, and as an adjunct professor at Zh ejiang University's Shanghai Advanced Research. DURATION:PT1H15M DTSTAMP:20240220T213214Z DTSTART;TZID=America/New_York:20240228T150500 LAST-MODIFIED:20240220T213214Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:InfiAgent: A Multi-Tool Agent for AI Operating Systems UID:CAL-8a0292fd-8d13410f-018d-417a1af6-00000d6ademobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=megan.deyncourt@duke.edu :Megan Deyncourt X-BEDEWORK-SPEAKER:Dr. Hongxia Yang\, Head of Large Models\, ByteDance US X-BEDEWORK-DUKE-SERIES:Statistical Science Proseminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240226T154511Z DESCRIPTION:Visualizations allow analysts to rapidly explore and make sens e of their data. The ways we visualize data directly influence the conclu sions we draw and decisions we make\; however\, our knowledge of how visu alization design influences data analysis is largely grounded in heuristi cs and intuition. My research instead empirically models how people inter pret visualized data to understand limitations in current visualization s ystems. We use these results to develop novel visualization systems that support accurate analysis of complex data and better scale to the needs o f modern analytics challenges by incorporating interactive statistical an alytics and immersive display technologies to increase the accessibility\ , scalability\, and pervasiveness of data-driven reasoning. In this talk\ , I will discuss our efforts towards improving exploratory data analysis guidelines and tools across a variety of domains. DURATION:PT1H DTSTAMP:20240226T154511Z DTSTART;TZID=America/New_York:20240301T153000 LAST-MODIFIED:20240226T154511Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Leveraging Visual Cognition in Data Visualization UID:CAL-8a0292fd-8d13410f-018d-e6176e9e-00002743demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-SPEAKER:Danielle Szafir\, Assistant Professor of Computer Scien ce at the University of North Carolina-Chapel Hill X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar Series X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240226T184141Z DESCRIPTION:Tree-based methods are popular nonparametric tools for capturi ng spatial heterogeneity and making predictions in multivariate problems. In unsupervised learning\, trees and their ensembles have also been appl ied to a wide range of statistical inference tasks\, such as multi-resolu tion sketching of distributional variations\, localization of high-densit y regions\, and design of efficient data compression schemes. In this tal k\, we will focus on the density estimation problem---a fundamental one i n unsupervised learning. We consider the optional P{\\'o}lya tree (Wong a nd Ma\, 2010) prior and the Dirichlet prior or their variations on indivi dual trees. First we show that Bayesian density trees can achieve minimax (up to a logarithmic term) convergence over the anisotropic Besov class\ , which implies that tree based methods can adapt to spatially inhomogene ous features of the underlying density function\, and can achieve fast co nvergence as the dimension increases. We will also introduce a novel Baye sian model for forests\, and show that for a class of anisotropic H{\\"o} lder continuous functions\, such type of density forests can achieve fast er convergence than trees. The convergence rate is adaptive in the sense that to achieve such a rate we do not need any prior knowledge of the smo othness level of the density. The Bayesian framework naturally provides a stochastic search algorithm over either the tree space or the forest one . For both Bayesian density trees and forests\, we will provide several n umerical results to illustrate their performance in the moderately high-d imensional case. DURATION:PT1H DTSTAMP:20240302T141658Z DTSTART;TZID=America/New_York:20240308T153000 LAST-MODIFIED:20240302T141658Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Bayesian trees and forests for unsupervised learning and their spa tial adaptation properties UID:CAL-8a0292fd-8d13410f-018d-e6b90689-0000359edemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SPEAKER:Linxi Liu\, Assistant Professor\, University of Pittsbu rgh X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240311T182540Z DESCRIPTION:Inverse scattering aims to infer information about a hidden ob ject by using the received scattered waves and training data collected fr om forward mathematical models. Recent advances in computing have led to increasing attention towards functional inverse inference\, which can rev eal more detailed properties of a hidden object. However\, rigorous studi es on functional inverse\, including the reconstruction of the functional input and quantification of uncertainty\, remain scarce. Motivated by an inverse scattering problem where the objective is to infer the functiona l input representing the refractive index of a bounded scatterer\, a new Bayesian framework will be discussed in the first part of this talk. It c ontains a surrogate model that takes into account the functional inputs d irectly through kernel functions\, and a Bayesian procedure that infers f unctional inputs through the posterior distribution. In the second part o f this talk\, we will introduce a novel procedure that\, given sparse dat a generated from a stationary deterministic nonlinear dynamical system\, can characterize specific local and/or global dynamic behavior with rigor ous probability guarantees. More precisely\, the sparse data is used to c onstruct a statistical surrogate model based on a Gaussian process (GP). The dynamics of the surrogate model is interrogated using combinatorial methods and characterized using algebraic topological invariants (Conley index). The GP predictive distribution provides a lower bound on the conf idence that these topological invariants\, and hence the characterized dy namics\, apply to the unknown dynamical system. DURATION:PT1H DTSTAMP:20240311T182540Z DTSTART;TZID=America/New_York:20240329T153000 LAST-MODIFIED:20240311T182540Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Surrogate modeling and uncertainty quantification for inverse prob lems and dynamical systems UID:CAL-8a0292fd-8d13410f-018e-2ec36393-000029c8demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:Ying Hung\, Professor\, Rutgers University X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whitesell X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240226T171835Z DESCRIPTION:In the talk I will present some of my recent works in the fiel d of Adversarial Risk Analysis. In the first part I will talk about Adve rsarial Classification. In multiple domains such as malware detection\, a utomated driving systems\, or fraud detection\, classification algorithms are susceptible to being attacked by malicious agents willing to perturb the value of instance covariates in search of certain goals. Such proble ms pertain to the field of adversarial machine learning and have been mai nly dealt with\, perhaps implicitly\, through game-theoretic ideas with s trong underlying common knowledge assumptions. These are not realistic in numerous application domains in relation to security. We present an alte rnative statistical framework that accounts for the lack of \nknowledge a bout the attacker's behavior using adversarial risk analysis concepts.\n\ nIn the second part I will discuss about an adversarial risk analysis fra mework for the software release problem. A major issue in software engine ering is the decision of when to release a software product to the market . This problem is complex due to\, among other things\, the uncertainty s urrounding the software quality and its faults\, the various costs involv ed\, and the presence of competitors. \n\nA general adversarial risk anal ysis framework is proposed to support a software developer in deciding wh en to release a product and showcased with an example. DURATION:PT1H DTSTAMP:20240311T193100Z DTSTART;TZID=America/New_York:20240405T153000 LAST-MODIFIED:20240311T193100Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Advances in Adversarial Risk Analysis UID:CAL-8a0292fd-8d13410f-018d-e66cefd4-00002d14demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=karen.whitesell@duke.edu :Karen Whiteselll X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-SPEAKER:Fabrizio Ruggeri\, CNR IMATI\, Milano\, Italy X-BEDEWORK-SUBMITTEDBY:kherndon for Statistical Science (agrp_StatisticalS cience) X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar Series END:VEVENT END:VCALENDAR