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BEGIN:VEVENT
SUMMARY:Dennis Davenport (Howard University)
DTSTART:20260915T180000Z
DTEND:20260915T190000Z
DTSTAMP:20261022T011258Z
UID:AI-CoNT/1
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/AI-CoNT/1/">
 Riordan Arrays\, Pseudo-Involutions\, and AI-Assisted Mathematical Discove
 ry</a>\nby Dennis Davenport (Howard University) as part of AI\, Combinator
 ics and Number Theory Seminar\n\n\nAbstract\nThe Riordan group is a group 
 of infinite lower triangular matrices that are defined by\ntwo generating 
 functions\, $g$ and $f$. The $k^{\\mathrm{th}}$ column of each matrix has 
 a generating function related\nto $gf^k$. There are many applications of R
 iordan arrays\, among other things they can be used to\ncount combinatoria
 l objects and to prove combinatorial identities\n\nIn this talk\, I will i
 ntroduce Riordan arrays and discuss recent joint work with Dewayne Dixon a
 nd\nMoussa Doumbia on pseudo-involutions. In the Appell subgroup\, the pse
 udo-involution condition\nreduces to the simple identity\n\\[\ng(z)g(-z)=1
 \,\n\\]\nwhich leads to a useful classification and several interesting in
 teger sequences. I will also discuss\nhow artificial intelligence can assi
 st mathematical discovery by helping generate examples\, identify\npattern
 s\, and suggest conjectures\, while exact computation and proof remain ess
 ential.\n
LOCATION:https://researchseminars.org/talk/AI-CoNT/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Matilde Lalín (Université de Montréal)
DTSTART:20260929T180000Z
DTEND:20260929T190000Z
DTSTAMP:20261022T011258Z
UID:AI-CoNT/2
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/AI-CoNT/2/">
 The arithmetic of Boyd’s Mahler measure conjectures: from data analysis 
 to machine learning</a>\nby Matilde Lalín (Université de Montréal) as p
 art of AI\, Combinatorics and Number Theory Seminar\n\n\nAbstract\nThe Mah
 ler measure is an invariant of multivariable rational functions defined by
  averaging\n$\\log |P|$ over the unit torus. Boyd conjectured that\, for\n
 \\[\nx+y+\\frac{1}{x}+\\frac{1}{y}+k\,\n\\]\nits Mahler measure is a ratio
 nal multiple $r_k L'(E_k\,0)$\, where $E_k$ is an associated\nelliptic cur
 ve. We study the arithmetic of the factors $r_k$ using a dataset of the fi
 rst\n250\,000 values of $k$\, combining large-scale statistical analysis w
 ith transformer-based\nexperiments. We observe several striking patterns i
 n their size and $p$-adic behavior\,\nand find that the neural networks re
 cover substantial arithmetic structure from the data\,\nand perhaps a bit 
 more. This is joint work with Alberto Alfarano\, Pablo Bianucci\, and\nBer
 end Ringeling.\n
LOCATION:https://researchseminars.org/talk/AI-CoNT/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Katherine Stange (University of Colorado\, Boulder)
DTSTART:20261027T180000Z
DTEND:20261027T190000Z
DTSTAMP:20261022T011258Z
UID:AI-CoNT/3
DESCRIPTION:by Katherine Stange (University of Colorado\, Boulder) as part
  of AI\, Combinatorics and Number Theory Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/AI-CoNT/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:David Roe (MIT)
DTSTART:20261110T190000Z
DTEND:20261110T200000Z
DTSTAMP:20261022T011258Z
UID:AI-CoNT/4
DESCRIPTION:by David Roe (MIT) as part of AI\, Combinatorics and Number Th
 eory Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/AI-CoNT/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Andrew Sutherland (MIT)
DTSTART:20261124T190000Z
DTEND:20261124T200000Z
DTSTAMP:20261022T011258Z
UID:AI-CoNT/5
DESCRIPTION:by Andrew Sutherland (MIT) as part of AI\, Combinatorics and N
 umber Theory Seminar\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/AI-CoNT/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Seewoo Lee (EPFL)
DTSTART:20261013T180000Z
DTEND:20261013T190000Z
DTSTAMP:20261022T011258Z
UID:AI-CoNT/6
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/AI-CoNT/6/">
 Some practical tips on digesting AI proofs: informal and formal aspects</a
 >\nby Seewoo Lee (EPFL) as part of AI\, Combinatorics and Number Theory Se
 minar\n\n\nAbstract\nIn this new era of proof abundance\, "digestion" has 
 become an important part of mathematics. As of October 2026\, LLM-generate
 d proofs are still hard to read\, and making them accessible requires cons
 iderable effort. On August 1\, OpenAI announced a result on the optimal ex
 ponent for both the Cohn–Elkies linear programming bound and the Bourgai
 n–Clozel–Kahane sign uncertainty principle. In this talk\, I will shar
 e my experience digesting this result. We will also go through its formali
 zation and how it can be improved.\n
LOCATION:https://researchseminars.org/talk/AI-CoNT/6/
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