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BEGIN:VEVENT
SUMMARY:Dennis Davenport (Howard University)
DTSTART:20260915T180000Z
DTEND:20260915T190000Z
DTSTAMP:20261021T234809Z
UID:AI-CoNT/1
DESCRIPTION:Title: <a href="https://master.researchseminars.org/talk/AI-Co
 NT/1/">Riordan Arrays\, Pseudo-Involutions\, and AI-Assisted Mathematical 
 Discovery</a>\nby Dennis Davenport (Howard University) as part of AI\, Com
 binatorics and Number Theory Seminar\n\n\nAbstract\nThe Riordan group is a
  group of infinite lower triangular matrices that are defined by\ntwo gene
 rating functions\, $g$ and $f$. The $k^{\\mathrm{th}}$ column of each matr
 ix has a generating function related\nto $gf^k$. There are many applicatio
 ns of Riordan arrays\, among other things they can be used to\ncount combi
 natorial objects and to prove combinatorial identities\n\nIn this talk\, I
  will introduce Riordan arrays and discuss recent joint work with Dewayne 
 Dixon and\nMoussa Doumbia on pseudo-involutions. In the Appell subgroup\, 
 the pseudo-involution condition\nreduces to the simple identity\n\\[\ng(z)
 g(-z)=1\,\n\\]\nwhich leads to a useful classification and several interes
 ting integer sequences. I will also discuss\nhow artificial intelligence c
 an assist mathematical discovery by helping generate examples\, identify\n
 patterns\, and suggest conjectures\, while exact computation and proof rem
 ain essential.\n
LOCATION:https://master.researchseminars.org/talk/AI-CoNT/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Matilde Lalín (Université de Montréal)
DTSTART:20260929T180000Z
DTEND:20260929T190000Z
DTSTAMP:20261021T234809Z
UID:AI-CoNT/2
DESCRIPTION:Title: <a href="https://master.researchseminars.org/talk/AI-Co
 NT/2/">The arithmetic of Boyd’s Mahler measure conjectures: from data an
 alysis to machine learning</a>\nby Matilde Lalín (Université de Montréa
 l) as part of AI\, Combinatorics and Number Theory Seminar\n\n\nAbstract\n
 The Mahler measure is an invariant of multivariable rational functions def
 ined 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 rational multiple $r_k L'(E_k\,0)$\, where $E_k$ is an associated\nellip
 tic curve. We study the arithmetic of the factors $r_k$ using a dataset of
  the first\n250\,000 values of $k$\, combining large-scale statistical ana
 lysis with transformer-based\nexperiments. We observe several striking pat
 terns in their size and $p$-adic behavior\,\nand find that the neural netw
 orks recover substantial arithmetic structure from the data\,\nand perhaps
  a bit more. This is joint work with Alberto Alfarano\, Pablo Bianucci\, a
 nd\nBerend Ringeling.\n
LOCATION:https://master.researchseminars.org/talk/AI-CoNT/2/
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BEGIN:VEVENT
SUMMARY:Katherine Stange (University of Colorado\, Boulder)
DTSTART:20261027T180000Z
DTEND:20261027T190000Z
DTSTAMP:20261021T234809Z
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://master.researchseminars.org/talk/AI-CoNT/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:David Roe (MIT)
DTSTART:20261110T190000Z
DTEND:20261110T200000Z
DTSTAMP:20261021T234809Z
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://master.researchseminars.org/talk/AI-CoNT/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Andrew Sutherland (MIT)
DTSTART:20261124T190000Z
DTEND:20261124T200000Z
DTSTAMP:20261021T234809Z
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://master.researchseminars.org/talk/AI-CoNT/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Seewoo Lee (EPFL)
DTSTART:20261013T180000Z
DTEND:20261013T190000Z
DTSTAMP:20261021T234809Z
UID:AI-CoNT/6
DESCRIPTION:Title: <a href="https://master.researchseminars.org/talk/AI-Co
 NT/6/">Some practical tips on digesting AI proofs: informal and formal asp
 ects</a>\nby Seewoo Lee (EPFL) as part of AI\, Combinatorics and Number Th
 eory Seminar\n\n\nAbstract\nIn this new era of proof abundance\, "digestio
 n" has become an important part of mathematics. As of October 2026\, LLM-g
 enerated proofs are still hard to read\, and making them accessible requir
 es considerable effort. On August 1\, OpenAI announced a result on the opt
 imal exponent for both the Cohn–Elkies linear programming bound and the 
 Bourgain–Clozel–Kahane sign uncertainty principle. In this talk\, I wi
 ll share my experience digesting this result. We will also go through its 
 formalization and how it can be improved.\n
LOCATION:https://master.researchseminars.org/talk/AI-CoNT/6/
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