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
RDATE:19350428T020000
RDATE:19360426T020000
RDATE:19370425T020000
RDATE:19380424T020000
RDATE:19390430T020000
RDATE:19400428T020000
RDATE:19410427T020000
RDATE:19460428T020000
RDATE:19470427T020000
RDATE:19480425T020000
RDATE:19490424T020000
RDATE:19500430T020000
RDATE:19510429T020000
RDATE:19520427T020000
RDATE:19530426T020000
RDATE:19540425T020000
RDATE:19550424T020000
RDATE:19560429T020000
RDATE:19570428T020000
RDATE:19580427T020000
RDATE:19590426T020000
RDATE:19600424T020000
RDATE:19610430T020000
RDATE:19620429T020000
RDATE:19630428T020000
RDATE:19640426T020000
RDATE:19650425T020000
RDATE:19660424T020000
RDATE:19670430T020000
RDATE:19680428T020000
RDATE:19690427T020000
RDATE:19700426T020000
RDATE:19710425T020000
RDATE:19720430T020000
RDATE:19730429T020000
RDATE:19740106T020000
RDATE:19750223T020000
RDATE:19760425T020000
RDATE:19770424T020000
RDATE:19780430T020000
RDATE:19790429T020000
RDATE:19800427T020000
RDATE:19810426T020000
RDATE:19820425T020000
RDATE:19830424T020000
RDATE:19840429T020000
RDATE:19850428T020000
RDATE:19860427T020000
RDATE:19870405T020000
RDATE:19880403T020000
RDATE:19890402T020000
RDATE:19900401T020000
RDATE:19910407T020000
RDATE:19920405T020000
RDATE:19930404T020000
RDATE:19940403T020000
RDATE:19950402T020000
RDATE:19960407T020000
RDATE:19970406T020000
RDATE:19980405T020000
RDATE:19990404T020000
RDATE:20000402T020000
RDATE:20010401T020000
RDATE:20020407T020000
RDATE:20030406T020000
RDATE:20040404T020000
RDATE:20050403T020000
RDATE:20060402T020000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:19181027T020000
RDATE:19181027T020000
RDATE:19191026T020000
RDATE:19201031T020000
RDATE:19210925T020000
RDATE:19220924T020000
RDATE:19230930T020000
RDATE:19240928T020000
RDATE:19250927T020000
RDATE:19260926T020000
RDATE:19270925T020000
RDATE:19280930T020000
RDATE:19290929T020000
RDATE:19300928T020000
RDATE:19310927T020000
RDATE:19320925T020000
RDATE:19330924T020000
RDATE:19340930T020000
RDATE:19350929T020000
RDATE:19360927T020000
RDATE:19370926T020000
RDATE:19380925T020000
RDATE:19390924T020000
RDATE:19400929T020000
RDATE:19410928T020000
RDATE:19450930T020000
RDATE:19460929T020000
RDATE:19470928T020000
RDATE:19480926T020000
RDATE:19490925T020000
RDATE:19500924T020000
RDATE:19510930T020000
RDATE:19520928T020000
RDATE:19530927T020000
RDATE:19540926T020000
RDATE:19551030T020000
RDATE:19561028T020000
RDATE:19571027T020000
RDATE:19581026T020000
RDATE:19591025T020000
RDATE:19601030T020000
RDATE:19611029T020000
RDATE:19621028T020000
RDATE:19631027T020000
RDATE:19641025T020000
RDATE:19651031T020000
RDATE:19661030T020000
RDATE:19671029T020000
RDATE:19681027T020000
RDATE:19691026T020000
RDATE:19701025T020000
RDATE:19711031T020000
RDATE:19721029T020000
RDATE:19731028T020000
RDATE:19741027T020000
RDATE:19751026T020000
RDATE:19761031T020000
RDATE:19771030T020000
RDATE:19781029T020000
RDATE:19791028T020000
RDATE:19801026T020000
RDATE:19811025T020000
RDATE:19821031T020000
RDATE:19831030T020000
RDATE:19841028T020000
RDATE:19851027T020000
RDATE:19861026T020000
RDATE:19871025T020000
RDATE:19881030T020000
RDATE:19891029T020000
RDATE:19901028T020000
RDATE:19911027T020000
RDATE:19921025T020000
RDATE:19931031T020000
RDATE:19941030T020000
RDATE:19951029T020000
RDATE:19961027T020000
RDATE:19971026T020000
RDATE:19981025T020000
RDATE:19991031T020000
RDATE:20001029T020000
RDATE:20011028T020000
RDATE:20021027T020000
RDATE:20031026T020000
RDATE:20041031T020000
RDATE:20051030T020000
RDATE:20061029T020000
END:STANDARD
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:19200101T000000
RDATE:19200101T000000
RDATE:19420101T000000
RDATE:19460101T000000
RDATE:19670101T000000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EWT
DTSTART:19420209T020000
RDATE:19420209T020000
END:DAYLIGHT
BEGIN:DAYLIGHT
TZOFFSETFROM:-0400
TZOFFSETTO:-0400
TZNAME:EPT
DTSTART:19450814T190000
RDATE:19450814T190000
END:DAYLIGHT
END:VTIMEZONE
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
BEGIN:VEVENT
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20240311T195848Z
DESCRIPTION:I discuss how one can use quantum circuits to accelerate multi
proposal MCMC and point to promising avenues of future research\, includi
ng quantum HMC\, quantum-accelerated nonreversible MCMC and quantum-acce
lerated locally-balanced MCMC.
DURATION:PT1H
DTSTAMP:20240311T195848Z
DTSTART;TZID=America/New_York:20240412T153000
LAST-MODIFIED:20240311T195848Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
stry 116
STATUS:CONFIRMED
SUMMARY:Quantum Markov chain Monte Carlo(s)
UID:CAL-8a0292fd-8d13410f-018e-2f18a91d-00003198demobedework@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:Andrew Holbrook\, Assistant Professor\, UCLA
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
END:VCALENDAR