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BEGIN:VEVENT
SUMMARY:Solon Barocas (Microsoft Research / Cornell University)
DTSTART:20200429T213000Z
DTEND:20200429T223000Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/1
DESCRIPTION:Title: What Is a Proxy and Why Is it a Problem?\nby Solon Barocas (Micro
soft Research / Cornell University) as part of NYU CDS Math and Democracy
Seminar\n\n\nAbstract\nWarnings about so-called ‘proxy variables’ have
become ubiquitous in recent policy debates about machine learning’s pot
ential to discriminate illegally. Yet it is far from clear what makes some
thing a proxy and why it poses a problem. In most cases\, commentators see
m to worry that even when a legally proscribed feature such as race is not
provided directly as an input into a machine learning model\, discriminat
ion on that basis may persist because non-proscribed features are correlat
ed with — that is\, serve as a proxy for — the proscribed feature. Ana
logizing to redlining\, commentators point out that zip codes can easily s
erve as a stand in for race. Yet\, unlike lenders\, a machine learning mod
el will not seize on zip codes because the model intends to discriminate o
n race\; it will only do so because zip codes also happen to be predictive
of the outcome of interest. So how are we to decide whether a variable is
serving as a proxy for race or as a legitimate predictor that just happen
s to be correlated with race? This question cuts to the core of discrimina
tion law\, posing both practical and conceptual challenges for resolving w
hether any observed disparate impact is justified when a decision relies o
n variables that exhibit any correlation with class membership. This paper
attempts to develop a more principled definition of proxy variables\, aim
ing to bring improved clarity to statistical\, legal\, and normative reaso
ning on the issue. It describes the various conditions that might create a
proxy problem and explores a wide range of possible responses. In so doin
g\, it reveals that any rigorous discussion of proxy variables requires ex
cavating the causal relationship that different commentators assume to exi
st between non-proscribed features\, proscribed features\, and the outcome
of interest. Joint with Margarita Boyarskaya and Hanna Wallach.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Momin Malik (Berkman Klein Center for Internet and Society at Harv
ard University)
DTSTART:20201005T213000Z
DTEND:20201005T224500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/2
DESCRIPTION:Title: A hierarchy of limitations in machine learning\nby Momin Malik (B
erkman Klein Center for Internet and Society at Harvard University) as par
t of NYU CDS Math and Democracy Seminar\n\n\nAbstract\nIn the immortal wor
ds of George E. P. Box (1979)\, “All models are wrong\, but some are use
ful.” This is an important lesson to recall amidst hopes and claims that
the impressive successes of machine learning will extend to wider branche
s of inquiry\, and that its high-dimension and low-assumption models can o
vercome what previously seemed to be insurmountable barriers. In this talk
\, I review the fundamental limitations with which all quantitative resear
ch into the social world must grapple\, and discuss how these limitations
manifest today.\n \nI cover sociological and philosophical aspects of the
process of quantification and modeling\, as well as technical aspects arou
nd implications of the bias-variance tradeoff and the effect of dependenci
es on cross-validation assessments of model performance. I metaphorically
structure the set of limitations as a tree\, where the root node is the ch
oice to undertake systematic inquiry\, the leaf nodes are specific methodo
logical approaches\, and each branch (qualitative/quantitative\, explanato
ry/predictive\, etc.) represents tradeoffs whose limitations percolate dow
nwards.\n \nThis talk will serve as a useful overview about modeling limit
ations and critiques\, as well as possible fixes\, for researchers in and
practitioners of data science\, statistics\, and machine learning. It will
also be useful as a primer for qualitative and theoretical social scienti
sts on what are solid grounds on which to accept or reject applications of
techniques from these areas\, as well as where there are promising areas
for developing new mixed methods approaches.\n\nThe talk format is a Zoom
webinar.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Audrey Malagon (Verified Voting / Virginia Wesleyan University)
DTSTART:20201026T213000Z
DTEND:20201026T224500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/3
DESCRIPTION:Title: Votes of Confidence: Leveraging Mathematics to Ensure Election Integr
ity\nby Audrey Malagon (Verified Voting / Virginia Wesleyan University
) as part of NYU CDS Math and Democracy Seminar\n\n\nAbstract\nOur democra
cy relies on fair elections in which every vote counts and ensures a peace
ful transition of power after the election. Concerns about foreign interfe
rence in our elections\, unreliable voting technology\, disinformation\, a
nd last minute changes during the COVID-19 pandemic make this election the
most challenging in recent history. In this talk\, we’ll discuss how
statistical post-election audits play a vital role in ensuring a fair and
trustworthy process and other ways that mathematics can help ensure the in
tegrity of our elections.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ariel Procaccia (Harvard University)
DTSTART:20201207T223000Z
DTEND:20201207T234500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/4
DESCRIPTION:Title: Democracy and the Pursuit of Randomness\nby Ariel Procaccia (Harv
ard University) as part of NYU CDS Math and Democracy Seminar\n\n\nAbstrac
t\nSortition is a storied paradigm of democracy built on the idea of choos
ing representatives through lotteries instead of elections. In recent year
s this idea has found renewed popularity in the form of citizens’ assemb
lies\, which bring together randomly selected people from all walks of lif
e to discuss key questions and deliver policy recommendations. A principle
d approach to sortition\, however\, must resolve the tension between two c
ompeting requirements: that the demographic composition of citizens’ ass
emblies reflect the general population and that every person be given a fa
ir chance (literally) to participate. I will describe our work on designin
g\, analyzing and implementing randomized participant selection algorithms
that balance these two requirements. I will also discuss practical challe
nges in sortition based on experience with the adoption and deployment of
our open-source system\, Panelot.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Kate Starbird (University of Washington)
DTSTART:20210308T223000Z
DTEND:20210308T234500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/5
DESCRIPTION:Title: Online Misinformation during Crisis Events: The “Perfect Storm” o
f Covid19 and Election2020\nby Kate Starbird (University of Washington
) as part of NYU CDS Math and Democracy Seminar\n\n\nAbstract\nThe past ye
ar has been a difficult one. A pandemic has taken millions of lives and di
srupted “normal” routines across the globe. In the United States\, we
have experienced an unprecedented political situation with a sitting Presi
dent refusing to concede after losing an election. Each of the events has
been accompanied by uncertainty and anxiety\, as well as massive amounts o
f false and misleading information. In this talk\, I will explore some of
the mechanics of online misinformation\, explaining why we are particularl
y vulnerable right now — due in part to the nature of these crises\, and
in part to the current structure of our information systems. Using exampl
es from both Covid19 and Election2020\, I will explain how we are living t
hrough a “perfect storm” for both misinformation and disinformation. A
nd I will describe how disinformation\, in particular\, can be an existent
ial threat to democratic societies. After laying out the problems\, I aim
to end on a more hopeful note\, with a call to action for researchers and
industry professionals to help “chip away” at this critical societal i
ssue.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mike Orrison (Harvey Mudd College)
DTSTART:20210426T213000Z
DTEND:20210426T224500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/6
DESCRIPTION:Title: Voting and Linear Algebra: Connections and Questions\nby Mike Or
rison (Harvey Mudd College) as part of NYU CDS Math and Democracy Seminar\
n\n\nAbstract\nVoting is something we do in a variety of settings and in a
variety of ways\, but it can often be difficult to see nontrivial relatio
nships between the different voting procedures we use. In this talk\, I wi
ll discuss how simple ideas from linear algebra and discrete mathematics c
an sometimes be used to unify different voting procedures\, and how doing
so leads to new insights and new questions in voting theory.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hakeem Angulu (Google / Metric Geometry and Gerrymandering Group)
DTSTART:20210510T213000Z
DTEND:20210510T224500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/7
DESCRIPTION:Title: The Voting Power Gap: Identifying Racial Gerrymandering with a Discre
te Voter Model\nby Hakeem Angulu (Google / Metric Geometry and Gerryma
ndering Group) as part of NYU CDS Math and Democracy Seminar\n\n\nAbstract
\nSection 2 of the Voting Rights Act of 1965 (VRA) prohibits voting practi
ces or procedures that discriminate based on race\, color\, or membership
in a language minority group\, and is often cited by plaintiffs seeking to
challenge racially-gerrymandered districts in court.\n\nIn 1986\, with Th
ornburg v. Gingles\, the Supreme Court held that in order for a plaintiff
to prevail on a section 2 claim\, they must show that:\n\n1. the racial or
language minority group is sufficiently numerous and compact to form a ma
jority in a single-member district\,\n2. that group is politically cohesiv
e\,\n3. and the majority votes sufficiently as a bloc to enable it to defe
at the minority’s preferred candidate.\n\nAll three conditions are notor
iously hard to show\, given the lack of data on how people vote by race.\n
\nIn the 1990s and early 2000s\, Professor Gary King’s ecological infere
nce method tackled the second condition: racially polarized voting\, or ra
cial political cohesion. His technique became the standard technique for a
nalyzing racial polarization in elections by inferring individual behavior
from group-level data. However\, for more than 2 racial groups or candida
tes\, that method hits computational bottlenecks.\n\nA new method of solvi
ng the ecological inference problem\, using a mixture of contemporary stat
istical computing techniques\, is demonstrated with this work. It is calle
d the Discrete Voter Model. It can be used for multiple racial groups and
candidates\, and has been shown to work well on randomly-generated mock el
ection data.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/7/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Tina Eliassi-Rad (Northeastern University)
DTSTART:20210329T213000Z
DTEND:20210329T224500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/8
DESCRIPTION:Title: What can Complexity Science do for Democracy?\nby Tina Eliassi-Ra
d (Northeastern University) as part of NYU CDS Math and Democracy Seminar\
n\n\nAbstract\nWe will discuss the following questions. What is democratic
backsliding? Is democratic backsliding an indicator of instability in the
democratic system? If so\, which processes potentially lead to this insta
bility? If we think of democracy as a complex system\, how can complexity
science help us understand and mitigate democratic backsliding? This talk
is based on these two papers: K. Wiesner et al. (2018) in European Journal
of Physics (https://doi.org/10.1088/1361-6404/aaeb4d) and T. Eliassi-Rad
et al. (2020) in Humanities & Social Sciences Communication (https://www.n
ature.com/articles/s41599-020-0518-0).\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/8/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Thomas Weighill (UNC Greensboro)
DTSTART:20211004T213000Z
DTEND:20211004T224500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/9
DESCRIPTION:Title: The Topology of Redistricting\nby Thomas Weighill (UNC Greensboro
) as part of NYU CDS Math and Democracy Seminar\n\n\nAbstract\nAcross the
nation\, legislatures and commissions are deciding where the Congressiona
l districts in their state will be for the next decade. Even under standar
d constraints such as contiguity and population balance\, they will have
exponentially many possible maps to choose from. Recent computational adva
nces have nonetheless made it possible to robustly sample from this vast s
pace of possibilities\, exposing the question of what a typical map looks
like to data analysis techniques. In this talk I will show how topological
data analysis (TDA) can help cut through the complexity and uncover key
political features of redistricting in a given state. This is joint work w
ith Moon Duchin and Tom Needham.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/9/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jeanne Clelland and Beth Malmskog (UC Boulder / Colorado College)
DTSTART:20211101T213000Z
DTEND:20211101T224500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/10
DESCRIPTION:Title: Colorado in Context: A case study in mathematics and fair redistrict
ing in Colorado\nby Jeanne Clelland and Beth Malmskog (UC Boulder / Co
lorado College) as part of NYU CDS Math and Democracy Seminar\n\n\nAbstrac
t\nHow do we measure or identify partisan bias in the boundaries of distri
cts for elected representatives? What outcomes are potentially “fair”
for a given region depends intimately on its particular human and politica
l geography. Ensemble analysis is a mathematical/statistical technique f
or putting potential redistricting maps in context of what can be expected
for maps drawn without partisan data. This talk will introduce the basi
cs of ensemble analysis\, describe some recent advances in creating repres
entative ensembles and quantifying mixing\, and discuss how our research g
roup has applied the technique in Colorado both in an academic framework a
nd as consultants to the 2021 Independent Legislative Redistricting Commis
sion.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ben Blum-Smith (NYU Center for Data Science)
DTSTART:20211122T223000Z
DTEND:20211122T234500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/11
DESCRIPTION:Title: Fair-division approaches to redistricting\nby Ben Blum-Smith (NY
U Center for Data Science) as part of NYU CDS Math and Democracy Seminar\n
\n\nAbstract\nProminent efforts to fight partisan gerrymandering in the US
have sought the help of a (hopefully) neutral arbiter: they have aimed ei
ther to elicit intervention from the courts\, or to delegate responsibilit
y for redistricting to a formally nonpartisan body such as an independent
commission. In this talk\, we discuss mechanisms to allow partisan actors
to produce a fair map without the involvement of such a neutral arbiter. I
nspired by the field of game theory\, and more specifically the study of f
air-division procedures\, the idea is to use the structured interplay of t
he parties' competing interests to produce a fair map. We survey the vario
us mechanisms that have been proposed in this fairly young line of researc
h\, propose new mechanisms with novel potentially-desirable features\, and
analyze them numerically.\n\nThis is joint work with Steven Brams\, Irfan
Jamil\, and Soledad Villar.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Greg Herschlag (Duke University)
DTSTART:20211213T223000Z
DTEND:20211213T234500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/12
DESCRIPTION:Title: Quantifying Gerrymandering: Advances in Sampling Graph Partitions fr
om Policy-Driven Measures\nby Greg Herschlag (Duke University) as part
of NYU CDS Math and Democracy Seminar\n\n\nAbstract\nGerrymandering is th
e process of manipulating political districts either to amplify the power
of a political group or suppress the representation of certain demographic
groups. Although we have seen increasingly precise and effective gerryman
ders\, a number of mathematicians\, political scientists\, and lawyers are
developing effective methodologies at uncovering and understanding the ef
fects of gerrymandered districts.\n\nThe basic idea behind these methods i
s to compare a given set of districts to a large collection of neutrally d
rawn plans. The process relies on three distinct components: First\, we de
termine rules for compliant redistricting plans along with codifying prefe
rences between these plans\; next\, we sample the space of compliant redis
tricting\nplans (according to our preferences) and generate a large collec
tion of non-partisan alternatives\; finally\, we compare the collection of
plans to a particular plan of interest. The first step\, though largely a
legal question of compliance\, provides interesting grounds for mathemati
cal translation between policies and probability measures\; the second and
third points create rich problems in the fields of applied\nmathematics (
sampling theory) and data analysis\, respectively.\n\nIn this talk\, I wil
l discuss how our research group at Duke has analyzed gerrymandering. I wi
ll discuss the sampling methods we employ and discuss several recent algor
ithmic advances. I will also mention several open problems and challenges
in this field. These sampling methods provide rich grounds both for mathem
atical exploration and development and also serve as a practical and relev
ant algorithm that can be employed to establish and maintain fair governan
ce.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/12/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Daryl DeFord (Washington State University)
DTSTART:20220307T223000Z
DTEND:20220307T234500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/13
DESCRIPTION:Title: Partisan Dislocation\, Competitivenes\, and Designing Ensembles for
Redistricting Analysis\nby Daryl DeFord (Washington State University)
as part of NYU CDS Math and Democracy Seminar\n\n\nAbstract\nComputational
redistricting techniques are playing an increasingly large role in the an
alysis and design of districting plans for legislative elections. In this
talk I will discuss recent work using Markov chain ensembles to evaluate t
radeoffs between redistricting criteria and a new measure\, partisan dislo
cation\, that evaluates plans by directly incorporating political geograph
y. Throughout\, I will demonstrate how examples of these methods in court
cases\, reform efforts\, and map construction highlight the important inte
rplay between mathematics\, computational methods\, political science\, an
d the law.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/13/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ana-Andreea Stoica (Columbia University)
DTSTART:20220328T213000Z
DTEND:20220328T224500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/14
DESCRIPTION:Title: Diversity and inequality in social networks\nby Ana-Andreea Stoi
ca (Columbia University) as part of NYU CDS Math and Democracy Seminar\n\n
\nAbstract\nOnline social networks often mirror inequality in real-world n
etworks\, from historical prejudice\, economic or social factors. Such dis
parities are often picked up and amplified by algorithms that leverage soc
ial data for the purpose of providing recommendations\, diffusing informat
ion\, or forming groups. In this talk\, I discuss an overview of my resear
ch involving explanations for algorithmic bias in social networks\, brief
ly describing my work in information diffusion\, grouping\, and general de
finitions of inequality. Using network models that reproduce inequality se
en in online networks\, we'll characterize the relationship between pre-ex
isting bias and algorithms in creating inequality\, discussing different a
lgorithmic solutions for mitigating bias.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/14/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jennifer Wilson / David McCune (The New School / William Jewell Co
llege)
DTSTART:20221107T223000Z
DTEND:20221107T233000Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/15
DESCRIPTION:Title: Ranked Choice Voting and the Spoiler Effect\nby Jennifer Wilson
/ David McCune (The New School / William Jewell College) as part of NYU CD
S Math and Democracy Seminar\n\n\nAbstract\nOne of the advantages commonly
cited about Ranked Choice Voting is that it prevents spoilers from affect
ing the outcome of an election. In this talk we will discuss what a spoile
r is and how it can be defined mathematically. Then we will examine how ra
nked choice voting performs relative to plurality voting based on this def
inition. We will approach this theoretically\, assuming impartial\, anony
mous culture and independent culture models\; through simulation using bot
h random and single-peaked models\; and empirically\, based on an analysis
of a large database of American ranked choice elections. All of these con
firm that ranked choice voting is superior to plurality based on the likel
ihood of the spoiler effect occurring.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/15/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jayshree Sarathy (Harvard University)
DTSTART:20221121T223000Z
DTEND:20221121T233000Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/16
DESCRIPTION:Title: Distrust in Noisy Numbers: Epistemic Disconnects Surrounding the Use
of Differential Privacy in the 2020 U.S. Census\nby Jayshree Sarathy
(Harvard University) as part of NYU CDS Math and Democracy Seminar\n\n\nAb
stract\nFor decades\, the U.S. Census Bureau has used disclosure avoidance
techniques in order to protect the confidentiality of individuals represe
nted in census data. Yet\, the Census Bureau's modernization of its disclo
sure avoidance procedures for its 2020 Census triggered a controversy that
is still underway. In this talk\, I argue that the move to differential p
rivacy exposed epistemic disconnects around what we identify as a "statist
ical imaginary\," destabilizing a network of practitioners that upholds th
e legitimacy of census data. I end by raising questions about how we can
—and must—re-imagine our statistical infrastructures going forward.\n\
nJayshree Sarathy is a 5th year PhD student in Computer Science (and Scien
ce & Technology Studies) at Harvard University. She is part of the Theory
of Computation group and OpenDP project\, and is currently a graduate fell
ow with the Harvard Edmond & Lily Safra Center for Ethics. Her research ex
plores the complexities of privacy and data access within socio-technical
systems.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/16/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sunoo Park (Columbia University)
DTSTART:20221205T223000Z
DTEND:20221205T233000Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/17
DESCRIPTION:Title: CANCELED: Scan\, Shuffle\, Rescan: Two-Prover Election Audits With U
ntrusted Scanners\nby Sunoo Park (Columbia University) as part of NYU
CDS Math and Democracy Seminar\n\n\nAbstract\nWe introduce a new way to co
nduct election audits using untrusted scanners. Post-election audits perfo
rm statistical hypothesis testing to confirm election outcomes. However\,
existing approaches are costly and laborious for close elections—often t
he most important cases to audit— requiring extensive hand inspection of
ballots. We instead propose automated consistency checks\, augmented by m
anual checks of only a small number of ballots. Our protocols scan each ba
llot twice\, shuffling the ballots between scans: a “two-scan” approac
h inspired by two-prover proof systems. We show that this gives strong sta
tistical guarantees even for close elections\, provided that (1) the permu
tation accomplished by the shuffle is unknown to the scanners and (2) the
scanners cannot reliably identify a particular ballot among others cast fo
r the same candidate. Our techniques could drastically reduce the time\, e
xpense\, and labor of auditing close elections\, which we hope will promot
e wider deployment.\n\nThis talk has been canceled. It will hopefully be r
escheduled.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/17/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Marion Campisi (San Jose State University)
DTSTART:20230306T223000Z
DTEND:20230306T234500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/18
DESCRIPTION:Title: The Geometry and Election Outcome (GEO) Metric\nby Marion Campis
i (San Jose State University) as part of NYU CDS Math and Democracy Semina
r\n\n\nAbstract\nWe introduce the Geography and Election Outcome (GEO) met
ric\, a new method for identifying potential partisan gerrymanders. In con
trast with currently popular methods\, the GEO metric uses both geographic
information about a districting plan as well as district-level partisan d
ata\, rather than just one or the other. We motivate and define the GEO me
tric\, which gives a count (a non-negative integer) to each political part
y. The count indicates the number of previously lost districts which that
party potentially could have had a 50% chance of winning\, without risking
any currently won districts\, by making reasonable changes to the input m
ap. We then analyze GEO metric scores for each party in several recent ele
ctions. We show that this relatively easy to understand and compute metric
can encapsulate the results from more elaborate analyses.\n\nMarion Campi
si is an Associate Professor in the department of Mathematics and Statisti
cs at San José State University. Her research interests lie in low dimens
ional topology\, knot theory and the mathematics of redistricting.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/18/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sunoo Park (Columbia University)
DTSTART:20230424T213000Z
DTEND:20230424T224500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/19
DESCRIPTION:Title: Scan\, Shuffle\, Rescan: Two-Prover Election Audits With Untrusted S
canners\nby Sunoo Park (Columbia University) as part of NYU CDS Math a
nd Democracy Seminar\n\n\nAbstract\nWe introduce a new way to conduct elec
tion audits using untrusted scanners. Post-election audits perform statist
ical hypothesis testing to confirm election outcomes. However\, existing a
pproaches are costly and laborious for close elections—often the most im
portant cases to audit— requiring extensive hand inspection of ballots.
We instead propose automated consistency checks\, augmented by manual chec
ks of only a small number of ballots. Our protocols scan each ballot twice
\, shuffling the ballots between scans: a “two-scan” approach inspired
by two-prover proof systems. We show that this gives strong statistical g
uarantees even for close elections\, provided that (1) the permutation acc
omplished by the shuffle is unknown to the scanners and (2) the scanners c
annot reliably identify a particular ballot among others cast for the same
candidate. Our techniques could drastically reduce the time\, expense\, a
nd labor of auditing close elections\, which we hope will promote wider de
ployment. Joint work with Douglas W. Jones\, Ronald L. Rivest\, and Adam S
ealfon.\n\nSunoo Park is a postdoctoral fellow at Columbia University and
visiting fellow at Columbia Law School. Her research is in security\, cryp
tography\, privacy\, and related law/policy issues. She received her J.D.
at Harvard Law School\, her Ph.D. in computer science at MIT\, and her B.A
. in computer science from the University of Cambridge.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/19/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ranthony Edmonds and Parker Edwards (MSRI/Duke University and Flor
ida Atlantic University)
DTSTART:20231106T224500Z
DTEND:20231106T234500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/20
DESCRIPTION:Title: Quantifying Communities of Interest in Electoral Redistricting\n
by Ranthony Edmonds and Parker Edwards (MSRI/Duke University and Florida A
tlantic University) as part of NYU CDS Math and Democracy Seminar\n\n\nAbs
tract\nCommunities of interest are groups of people\, such as ethnic\, rac
ial\, and economic groups\, with common sets of concerns that may be affec
ted by legislation. Many states have requirements to preserve communities
of interest as part of their redistricting process. While some states coll
ect data about communities of interest in the form of public testimony\, t
here are no states to our knowledge which systematically collect\, aggrega
te\, and summarize spatialized testimony on communities of interest when d
rawing new districting plans.\n\nDuring the 2021 redistricting cycle\, our
team worked to quantify communities of interest by collecting and synthes
izing thousands of community maps in partnership with grassroots organizat
ions and/or government offices. In most cases\, the spatialized testimony
collected included both geographic and semantic data–a spatial represent
ation of a community as a polygon\, as well as a written narrative descrip
tion of that community. In this talk\, we outline our aggregation pipeline
that started with spatialized testimony as input\, and output processed c
ommunity clusters for a given state with geographic and semantic cohesion.
\n\nBios: Dr. Ranthony A.C. Edmonds is a Berlekamp Postdoctoral Researcher
at the Simons Laufer Mathematical Sciences Institute affiliated with the
Department of Mathematics at Duke University. She earned a PhD in Mathe
matics in 2018 from the University of Iowa\, an MS in Mathematical Science
s from Eastern Kentucky University in 2013\, and a BA in English and a BS
in Mathematics from the University of Kentucky in 2011. Her research inter
ests include applied algebraic topology\, data science\, commutative ring
theory\, and mathematics education. She is deeply invested in quantitative
justice\, that is\, using mathematical tools to address societal issues r
ooted in inequity. Her current work in quantitative justice involves appli
cations of mathematics and statistics to electoral redistricting.\n\nDr.
Parker Edwards is an Assistant Professor in the Department of Mathematica
l Sciences at Florida Atlantic University. His research focuses on both th
eory and applications for combining machine learning with tools from compu
tational algebraic topology and geometry to analyze complex and high-dimen
sional data sets. He received a PhD in Mathematics from the University of
Florida in 2020 and MSc in Mathematics and the Foundations of Computer Sci
ence from the University of Oxford in 2016.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/20/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sam Wang (Princeton and Electoral Innovation Lab)
DTSTART:20231213T223000Z
DTEND:20231213T233000Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/21
DESCRIPTION:Title: Dimensionality reduction reveals dependence of voter polarization on
political context\nby Sam Wang (Princeton and Electoral Innovation La
b) as part of NYU CDS Math and Democracy Seminar\n\n\nAbstract\nPolitical
dynamics in the U.S. have become highly polarized\, despite the fact that
individual voters can have complex views. To capture the dimensionality of
how voters express their preferences\, we are analyzing over 400 ranked-c
hoice elections\, in which voters rank candidates in order of preference.
We find that most voters act as if they share a representation of candidat
es on a single axis. Voters each have a place on that axis\, in the aggreg
ate defining a spectrum of simple political behavior. Voter spectra are mo
re bimodal for executive and federal offices than for local offices\, sugg
esting that polarization of voter behavior is strongly shaped by available
choices. We are now investigating how candidate and voter behavior may be
shaped by incentives arising from ranked-choice voting and other reforms.
Such shaping would suggest practical strategies to reduce political polar
ization.\n\nSam Wang has been a professor at Princeton University since 20
00\, and director of the Electoral Innovation Lab since 2020. He holds a B
.S. in physics from the California Institute of Technology and a Ph.D. in
neuroscience from Stanford University. He has published over 100 articles
spanning neuroscience\, elections\, and democracy reform. A central featur
e of his research is the use and development of statistical tools for deal
ing with large\, complex data sets. In 2004\, he pioneered methods for the
aggregation of state polls to predict U.S. presidential elections. In 201
2 he recognized new\, systematic distortions in representation in the U.S.
House\, leading to the creation of the Princeton Gerrymandering Project.
In 2020 these projects were subsumed into the Electoral Innovation Lab\, w
hose mission is to create and apply a practical science of democracy refor
m.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/21/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Abigail Hickok and Mason Porter (Columbia and UCLA)
DTSTART:20240506T213000Z
DTEND:20240506T223000Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/22
DESCRIPTION:Title: Topological Data Analysis of Voting-Site Coverage\nby Abigail Hi
ckok and Mason Porter (Columbia and UCLA) as part of NYU CDS Math and Demo
cracy Seminar\n\n\nAbstract\nIn many cities in the United States\, it can
take a very long time to go to a polling site to cast a vote in an electio
n. To find such "voting deserts" in an algorithmic way\, we use persistent
homology (PH)\, which is a type of topological data analysis (TDA) that a
llows one to detect "holes" in data. In this talk\, we'll give an introduc
tion to TDA and PH. We will then discuss our recent work on PH to detect v
oting deserts and in the coverage of other resources.\n\n(Use interactive
livestream for Q&A but the view-only livestream should have better sound.)
\n\nAbigail Hickok is an NSF postdoctoral fellow in the Department of Math
ematics at Columbia University. Prior to joining Columbia\, she completed
a PhD in applied mathematics at UCLA in 2023\, and she received her underg
raduate degree in mathematics at Princeton in 2018. Her research is on the
theory and applications of geometric and topological data analysis.\n\nMa
son Porter is a professor in the Department of Mathematics at University o
f California\, Los Angeles (UCLA). He earned a B.S. in Applied Mathematics
from Caltech in 1998 and a Ph.D. from the Center for Applied Mathematics
at Cornell University in 2002. Mason held postdoctoral positions at Georgi
a Tech\, the Mathematical Sciences Research Institute\, and California Ins
titute of Technology (Caltech). He joined as faculty at University of Oxfo
rd in 2007 and moved to UCLA in 2016. Mason is a Fellow of the American Ma
thematical Society\, the American Physical Society\, and the Society for I
ndustrial and Applied Mathematics. In recognition of his mentoring of unde
rgraduate researchers\, Mason won the 2017 Council on Undergraduate Resear
ch (CUR) Faculty Mentoring Award in the Advanced Career Category in the Ma
thematics and Computer Science Division. To date\, 26 students have comple
ted their Ph.D. degrees under Mason's mentorship\, and Mason has also ment
ored several postdocs\, more than 30 masters students\, and more than 100
undergraduate students on various research projects. Mason's research inte
rests lie in theory and (rather diverse) applications of networks\, comple
x systems\, and nonlinear systems.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/22/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Cory McCartan (NYU Center for Data Science)
DTSTART:20240513T213000Z
DTEND:20240513T223000Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/23
DESCRIPTION:Title: Estimating Racial Disparities When Race is Not Observed\nby Cory
McCartan (NYU Center for Data Science) as part of NYU CDS Math and Democr
acy Seminar\n\n\nAbstract\nDiscovering and quantifying racial disparities
is critical to ensuring equitable distribution of public goods and servic
es\, and building fair decision-making algorithms and processes. But in
many important contexts\, data about race is not available at the individu
al level. Methods exist to predict individuals' race from attributes lik
e their name and location\, but these tools create their own set of stat
istical challenges\, which if not addressed can significantly understate
or overstate the size of racial disparities. This talk will discuss the
se challenges and introduce new methodology to address them\, allowing fo
r accurate inference of racial disparities in datasets without racial info
rmation. The authors have worked with the U.S. Treasury Department to ap
ply the new method to millions of individual tax returns to estimate dispa
rities in who claims the home mortgage interest deduction\, the most expe
nsive individual deduction in the federal tax code.\n\nCory McCartan is a
Faculty Fellow at CDS and will join the Penn State Department of Statistic
s in July. He works on methodological and applied problems in the socia
l sciences\, including gerrymandering\, electoral reform\, privacy of publ
ic data\, and racial disparities.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/23/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Lalitha Sankar (Arizona State University)
DTSTART:20250224T223000Z
DTEND:20250224T234500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/24
DESCRIPTION:Title: Understanding Last Layer Retraining Methods for Fair Classification:
Theory and Algorithms\nby Lalitha Sankar (Arizona State University) a
s part of NYU CDS Math and Democracy Seminar\n\n\nAbstract\nLast-layer ret
raining (LLR) methods have emerged as an efficient framework for ensuring
fairness and robustness in deep models. In this talk\, we present an overv
iew of existing methods and provide theoretical guarantees for several pro
minent methods. Under the threat of label noise\, either in the class or d
omain annotations\, we show that these naive methods fail. To address thes
e issues\, we present a new robust LLR method in the framework of two-stag
e corrections and demonstrate that it achieves state-of-the-art performanc
e under domain label noise with minimal data overhead. Finally\, we demons
trate that class label noise causes catastrophic failures even with robust
two-stage methods\, and propose a drop-in label correction which outperfo
rms existing methods with very low computational and data cost.\n\nLalitha
Sankar is a Professor in the School of Electrical\, Computer and Energy E
ngineering at Arizona State University. She joined ASU as an assistant pro
fessor in fall of 2012\, and was an associate professor from 2018-2023. Sh
e received a bachelor's degree from the Indian Institute of Technology\,
Bombay\, a master's degree from the University of Maryland\, and a doctora
te from Rutgers University in 2007. Following her doctorate\, Sankar was
a recipient of a three-year Science and Technology Teaching Postdoctoral F
ellowship from the Council on Science and Technology at Princeton Universi
ty\, following which she was an associate research scholar at Princeton. P
rior to her doctoral studies\, she was a senior member of technical staff
at AT&T Shannon Laboratories.\n\nSankar's research interests are at the in
tersection of information and data sciences including a background in sign
al processing\, learning theory\, and control theory with applications to
the design of machine learning algorithms with algorithmic fairness\, priv
acy\, and robustness guarantees. Her research also applies such methods to
complex networks including the electric power grid and healthcare systems
. \n\nFor her doctoral work\, she received the 2007-2008 Electrical Engine
ering Academic Achievement Award from Rutgers University. She received the
IEEE Globecom 2011 Best Paper Award for her work on privacy of side-infor
mation in multi-user data systems. She was awarded the National Science Fo
undation CAREER award in 2014 for her project on privacy-guaranteed distri
buted interactions in critical infrastructure networks such as the Smart G
rid. She has led an NSF Institute on Data-intensive Research in Science an
d Engineering (I-DIRSE)\, is a recipient of an NSF SCALE MoDL (Mathematics
of Deep Learning) grant\, and a Google AI for Social Good grant. Sankar w
as a distinguished lecturer for the IEEE Information Theory Society from 2
020-2022. She serves as an Associate Editor for the IEEE Transactions on I
nformation Forensics and Security\, IEEE Information Theory Transactions\,
and was an AE for the IEEE BITS Magazine until August 2024.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/24/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Matthew Dahl
DTSTART:20250331T213000Z
DTEND:20250331T224500Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/25
DESCRIPTION:Title: Large Legal Fictions: Detecting Legal Hallucinations in Large Langua
ge Models\nby Matthew Dahl as part of NYU CDS Math and Democracy Semin
ar\n\n\nAbstract\nDo large language models (LLMs) know the law? LLMs are i
ncreasingly being used to augment legal practice\, but their revolutionary
potential is threatened by the presence of legal "hallucinations" -- text
ual output that is not consistent with the content of the law. In this tal
k\, I theorize the provenance and nature of these hallucinations and discu
ss methods for detecting them in LLM outputs. I then share results from th
ree experiments auditing off-the-shelf LLMs and industry retrieval-augment
ed generation (RAG) models\, showing that legal errors remain widespread.
I conclude by emphasizing the need for empirical evidence in an age of eve
r-increasing hype about AI's ability to replace lawyers and expand access
to justice.\n\nMatthew Dahl is a JD/PhD student at Yale Law School and Yal
e Department of Political Science. His research on AI\, judicial behavior\
, and legal citation analysis has been published in the Journal of Empiric
al Legal Studies and the Journal of Legal Analysis. Before coming to Yale\
, he was a Fair Housing Fellow at Brancart & Brancart and received his BA
from Pomona College.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/25/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bailey Passmore
DTSTART:20250428T213000Z
DTEND:20250428T223000Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/26
DESCRIPTION:Title: Public data & human rights\nby Bailey Passmore as part of NYU CD
S Math and Democracy Seminar\n\n\nAbstract\nFor the last 30 years\, the Hu
man Rights Data Analysis Group (HRDAG) has been using statistics and data
science to support human rights advocacy campaigns around the world. While
our international work typically involves casualty counts and using advan
ced statistical techniques to estimate undocumented victims\, our U.S. wor
k often involves using public data to report human rights violations exper
ienced by those who are still living. Now\, the California Racial Justice
Act of 2020 has opened up even more opportunities for us to contribute to
campaigns for racial justice\, particularly for those affected by racial b
ias in criminal legal proceedings. Since early 2024\, HRDAG has been colla
borating with public defenders to provide support for RJA claims for their
clients. This led to our first time providing expert testimony in a U.S.
courtroom in February 2025\, when we presented and defended 3 simple stati
stics based on the District Attorney's case data and county census data. B
ailey will discuss the model HRDAG uses for obtaining and analyzing public
data to address local human rights concerns\, as well as their experience
working on an RJA case.\n\nBio: Bailey Passmore has been a data scientist
at the Human Rights Data Analysis Group (“HRDAG”) since January 2022.
While at HRDAG\, they have designed reproducible and transparent data pro
cessing streams that include a variety of tasks\, such as scraping data fr
om public transparency platforms\, extracting structured data from unstruc
tured document collections\, extracting key information from text data usi
ng LLMs\, database deduplication and entity resolution\, version resolutio
n\, and producing statistical analyses that speak to patterns of racial bi
as. Prior to their position at HRDAG\, Bailey worked as an undergraduate D
ata Science and Research Consultant for the San Diego Supercomputer Center
\, where they mined\, cleaned\, and analyzed system performance data and p
repared the findings for the Practice and Experience in Advanced Research
Computing (PEARC) conferences. Bailey graduated from the University of Cal
ifornia\, San Diego with a bachelor of science degree in Cognitive Science
\, after transferring with a background in Mathematics and Computer Scienc
e.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/26/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Daphne Skipper
DTSTART:20251110T223000Z
DTEND:20251110T233000Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/27
DESCRIPTION:Title: Geographic Access to Polling\nby Daphne Skipper as part of NYU C
DS Math and Democracy Seminar\n\n\nAbstract\nLonger travel distances to po
lling places can discourage people from voting\, and these effects tend to
fall hardest on minority communities. In this talk\, I will share a new a
pproach for selecting polling sites that promote more equitable geographic
access to voting. Our method does two things: it assesses how fair a give
n set of polling sites is\, and it identifies the optimal set of sites to
open from a list of possible locations. The key idea is to borrow a concep
t from the environmental justice literature\, the Kolm–Pollak Equally Di
stributed Equivalent (EDE)\, which is designed to compare distributions of
disamenities such as exposure to air pollution. By adapting this measure\
, we can strike a balance between minimizing the average distance to polls
and improving access for residents who live farthest away. I will introdu
ce the intuition behind the Kolm–Pollak EDE\, show how it fits into an o
ptimization model that scales to city- and county-level problems\, and dem
onstrate its use through a case study of early voting sites in DeKalb Coun
ty\, Georgia\, during the 2020\, 2022\, and 2024 elections.\n\nDaphne Skip
per is a mathematician and operations researcher specializing in combinato
rial and global optimization. Her theoretical work examines nonlinear mode
ling structures that arise across a wide range of optimization problems\,
with the goal of providing practical insight into how these structures are
handled in models and algorithms. She applies these insights to large\, c
omplex systems where better modeling translates into real-world impact. So
me examples of her applied projects include maximizing the impact of pollu
tion-mitigation efforts in the Chesapeake Bay watershed\, optimizing gas m
ixing and network operations to better meet demand\, and designing equitab
le facility-location models that balance efficiency with fairness. In this
latter area\, her work spans methodological development\, equitable selec
tion of election polling sites\, and improving access to grocery stores in
food deserts. Her research has appeared in leading journals such as Natur
e Communications\, the Election Law Journal\, and Mathematical Programming
\, reflecting her commitment to applying mathematical rigor to problems of
societal importance. Daphne lives and works in Annapolis\, Maryland.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/27/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Zarina Dhillon
DTSTART:20251124T223000Z
DTEND:20251124T233000Z
DTSTAMP:20260422T212557Z
UID:MathandDemoc/28
DESCRIPTION:Title: Evaluating Methods Used to Quantify Racial Segregation\nby Zarin
a Dhillon as part of NYU CDS Math and Democracy Seminar\n\n\nAbstract\nRac
ial segregation has long been a problem in communities across the United S
tates\, and in understanding how it is quantified we enhance our ability t
o offer proposals for eradication. Many metrics have been developed for me
asurement\, but none fully capture the nuances of this complicated issue:
This work provides an overview of four mathematical approaches that have b
een developed to study segregation\, explains how they function\, and comp
ares/contrasts their effectiveness in various situations in order to deter
mine which best succeeds. An additional focus lies in a case study of Los
Angeles (LA) County. It was found that attempts to further standardize out
puts erases crucial data\, and compressing this issue into one score is no
t representative of its complexity. This suggests that future exploration
should attempt to study segregation more comprehensively rather than disti
lling an incredibly complicated and important issue into a single statisti
c.\n\nZarina Dhillon is earning her Masters in Applied Statistics at NYU S
teinhart with a concentration on data science for social impact. Zarina is
also a Parke Research Fellow in the Brennan Center for Justice's Democrac
y Program\, where she focuses on voter turnout and redistricting. She earn
ed her Bachelors in Mathematics from Claremont McKenna College as a proud
transfer student from Santa Barbara City College\, where she earned nine a
ssociates degrees spanning economics\, philosophy\, communications\, psych
ology\, and mathematics.\n
LOCATION:https://researchseminars.org/talk/MathandDemoc/28/
END:VEVENT
END:VCALENDAR