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DESCRIPTION:Lunch\n\nLunch will be served at 11:45 AM.\n\nAbstract\n\nThe t
ight connection between Discrete Mathematics and Theoretical Computer Scie
nce has been fruitful in the development of both areas in the recent decad
es. I will describe several examples illustrating this fact.\n\n\nSpeaker
Bio\n\nNoga Alon is a Professor of Mathe
matics at Princeton University and a Baumritter Professor Emeritus of Math
ematics and Computer Science at Tel Aviv University\, Israel. His researc
h interests are mainly in Combinatorics\, Graph Theory and their applicati
ons in Theoretical Computer Science. His main contributions include the st
udy of expander graphs and their applications\, the investigation of deran
domization techniques\, the foundation of streaming algorithms\, the devel
opment and applications of algebraic and probabilistic methods in Discrete
Mathematics and the study of problems in Information Theory\, Combinatori
al Geometry\, Combinatorial Number Theory and Computational Learning.\n\nP
rof. Alon is a member of the Israel Academy of Sciences and Humanities and
of the Academia Europaea\, and an honorary member of the Hungarian Academ
y of Sciences. He received several awards\, two recent ones are the 2022 S
haw Prize in Mathematical Sciences and the 2022 Knuth Prize for outstandin
g contributions to the foundations of computer science.\n
UID:040000008200E00074C5B7101A82E00800000000828A030AE7ECDA01000000000000000
0100000009AEA366F97D2CC4EBCA7783870BEA8B9
SUMMARY:The Fruitful Interplay between Discrete Mathematics and Theoretical
Computer Science
DTSTART;TZID=UTC:20240826T160000
DTEND;TZID=UTC:20240826T170000
CLASS:PUBLIC
PRIORITY:5
DTSTAMP:20240910T015633Z
TRANSP:OPAQUE
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DESCRIPTION:The following information couldn’t be added to Exchange:\ntim
e zone:Floating
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SUMMARY:jg197 - wfh
DTSTART;VALUE=DATE:20240903
DTEND;VALUE=DATE:20240904
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DTSTAMP:20240910T015633Z
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DESCRIPTION:Lunch\n\nLunch will be served at 11:45 AM.\n\nAbstract\n\nPolit
ical discourse is the soul of democracy\, but misunderstanding and conflic
t can fester in divisive conversations. The widespread shift to online dis
course exacerbates many of these problems and corrodes the capacity of div
erse societies to cooperate in solving social problems. Scholars and civil
society groups promote interventions that make conversations less divisiv
e or more productive\, but scaling these efforts to online discourse is ch
allenging. This talk will describe a large-scale experiment that demonstra
tes how online conversations about divisive topics can be improved with fo
undation models. Specifically\, my colleagues and I employed a fine-tuned
large language model to make real-time\, evidence-based recommendations ab
out how to bridge social divides during online conversations between two p
eople discussing gun control in an online forum. Respondents could accept
such recommendations\, ignore them\, or revise their posts after seeing th
e suggested edits. Our results indicate this intervention improved reporte
d conversation quality\, promoted democratic reciprocity\, and improved th
e tone of conversations\, without systematically changing the content of t
he conversation or moving people’s policy attitudes. Finally\, I will di
scuss a half year replication of this experiment on a large social media p
latform and discuss other opportunities to employ foundation models to red
uce conflict in online settings and study complex social systems.\n\nSpeak
er Bio\n\nChris Bail is
a Professor of Sociology\, Political Science\, and Public Policy at Duke
University\, where he founded the Polarization Lab and the Summer Institut
es in Computational Social Science. He studies how artificial intelligence
shapes human behavior in a range of different settings—and social media
platforms in particular.\n
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010000000EC9A227B08DB0E45BDCF06BA8FDA7FA2
SUMMARY:Leveraging LLM-Assistants to Mediate Conflict in Online Discussions
about Divisive Topics
DTSTART;TZID=UTC:20240916T160000
DTEND;TZID=UTC:20240916T170000
CLASS:PUBLIC
PRIORITY:5
DTSTAMP:20240910T015633Z
TRANSP:OPAQUE
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DESCRIPTION:Lunch\n\nLunch will be served at 11:45 AM.\n\nAbstract\n\nWe mo
del real-world data markets\, where sellers post fixed prices and buyers a
re free to purchase from any set of sellers\, as a simultaneous game. A ke
y component here is the negative externality buyers induce on one another
due to data purchases. Starting with a simple setting where buyers know th
eir valuations a priori\, we characterize both the existence and welfare p
roperties of the pure Nash equilibrium in the presence of such externality
. While the outcomes are bleak without any intervention\, mirroring the li
mitations of current data markets\, we prove that for a standard class of
externality functions\, platforms intervening through a transaction cost c
an lead to a pure equilibrium with strong welfare guarantees. We next cons
ider a more realistic setting where buyers learn their valuations over tim
e through market interactions. Our intervention is feasible here as well\,
and we consider learning algorithms to achieve low regret concerning both
individual and cumulative utility metrics. Lastly\, we analyze the promis
es of this intervention under a much richer externality model.\n\nThis is
based on joint work with Safwan Hossain. Link to the paper: https://arxiv.
org/pdf/2302.08012 .\n\nSpeaker Bio\n\nYiling Chen is a Gordon McKay Professor of Compute
r Science at Harvard University. She received her Ph.D. in Information Sci
ences and Technology from the Pennsylvania State University. Prior to work
ing at Harvard\, she spent two years at Yahoo! Research in New York City.
Her research lies in the intersection of computer science\, economics\, an
d other social sciences\, with a focus on social aspects of computational
systems. She was a recipient of The Penn State Alumni Association Early Ca
reer Award and was selected by IEEE Intelligent Systems as one of "AI's 10
to Watch” early in her career. Her work received best paper awards at A
CM EC\, AAMAS\, ACM FAT* (now ACM FAccT) and ACM CSCW conferences. She was
a program co-chair for the 2013 Conference on Web and Internet Economics
(WINE’13)\, the 2016 ACM Conference on Economics and Computation (EC’1
6)\, the 2018 AAAI Conference on Human Computation and Crowdsourcing (HCOM
P’18) and the 2023 AAAI Conference on Artificial Intelligence (AAAI-23)
and has served as an associate editor for several journals.\n
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010000000F4761BBA0676114E8A1B42602B1DB6B4
SUMMARY:Equilibrium of Data Markets with Externality
DTSTART;TZID=UTC:20240927T160000
DTEND;TZID=UTC:20240927T170000
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PRIORITY:5
DTSTAMP:20240910T015633Z
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DESCRIPTION:Connect and celebrate with your classmates at this casual\, fun
reception honoring our returning alumni. The reception will feature finge
r food\, beer\, wine\, and other beverages. Stop in at the Hall of Science
\, Levine Science Research Center (308 Research Dr\, Durham) anytime from
5:00 to 7:00. This event is free but please let us know how many plan to a
ttend by registering.\n
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010000000DD16F1476F358A4C8430AC30B445B181
SUMMARY:Duke Computer Science Alumni Homecoming Reception
DTSTART;TZID=UTC:20240927T210000
DTEND;TZID=UTC:20240927T230000
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PRIORITY:5
DTSTAMP:20240910T015633Z
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DESCRIPTION:Lunch\n\nLunch will be served at 11:45 AM.\n\nAbstract\n\nA few
years ago\, a group of theoretical computer scientists posted a paper on
the Arxiv with the strange-looking title "MIP* = RE"\, surprising and impa
cting not only complexity theory but also some areas of math and physics.
Specifically\, it resolved\, in the negative\, the "Connes' embedding conj
ecture" in the area of von-Neumann algebras\, and the "Tsirelson problem"
in quantum information theory. It further connects Turing's seminal 1936 p
aper which defined algorithms\, to Einstein's 1935 paper with Podolsky and
Rosen which challenged quantum mechanics. You can find the paper here: ht
tps://arxiv.org/abs/2001.04383\n\nAs it happens\, both acronyms MIP
* and RE represent proof systems\, of a very different nature. To explain
them\, we'll take a meandering journey through the classical and modern de
finitions of proof. I hope to explain how the methodology of computational
complexity theory\, especially modeling and classification (of both probl
ems and proofs) by algorithmic efficiency\, naturally leads to the generat
ion of new such notions and results (and more acronyms\, like NP). A speci
al focus will be on notions of proof which allow interaction\, randomness\
, and errors\, and their surprising power and magical properties.\n\nSpeak
er Bio\n\nAvi Wigderson is the Her
bert H. Maas Professor in the School of Mathematics at the Institute for A
dvanced Study in Princeton\, New Jersey\, where he has been teaching since
1999. He also served in the Computer Science Institute at the Hebrew Uni
versity in Jerusalem from 1986-2003. He earned his Ph.D. in Computer Scie
nce from Princeton University in 1983. His research interests include the
Computational Complexity Theory\, Algorithms and Optimization\, Randomnes
s and Cryptography\, Parallel and Distributed Computation\, Combinatorics
and Graph Theory\, and Connections of CS Theory with Math and Science. Av
is was the 2023 winner of the A.M. Turing Award from the Association for C
omputing Machinery (ACM).\n
UID:040000008200E00074C5B7101A82E008000000001D180CBBABDEDA01000000000000000
0100000008F2930A7BCE91440BF65EA2DA50F4878
SUMMARY:The Value of Errors in Proofs
DTSTART;TZID=UTC:20241011T160000
DTEND;TZID=UTC:20241011T170000
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