BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.com//juliacon-2026//speaker//WGRKMC
BEGIN:VTIMEZONE
TZID:CET
BEGIN:STANDARD
DTSTART:20001029T040000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-juliacon-2026-RXG7AD@pretalx.com
DTSTART;TZID=CET:20260812T113000
DTEND;TZID=CET:20260812T120000
DESCRIPTION:Wouldn’t it be great if Julia could program itself? \nYou sim
 ply tell it what you want\, Julia magic happens\, and you get correct-by-c
 onstruction code.\nIn this talk\, we introduce `Herb.jl`\, a unifying prog
 ram synthesis library written in Julia\, that gets us closer to this goal.
 \nWhile we are not fully there yet\, we have significantly progressed sinc
 e our last talk at JuliaCon 2024.
DTSTAMP:20260502T094003Z
LOCATION:Room 2
SUMMARY:What’s new with Herb.jl: Teaching Programs how to Program with Pr
 ogram Synthesis - Tilman Hinnerichs\, Reuben Gardos Reid
URL:https://pretalx.com/juliacon-2026/talk/RXG7AD/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-juliacon-2026-9MGLXG@pretalx.com
DTSTART;TZID=CET:20260813T123000
DTEND;TZID=CET:20260813T124500
DESCRIPTION:Julia has great tools for simulating and analyzing many kinds o
 f dynamical systems.\nHowever\, discrete\, finite-state systems\, i.e.\, s
 ystems made of many interacting parts that each have a small number of pos
 sible states\, are less well supported.  The class of GDS includes many we
 ll-known formalisms\, such as Boolean networks\, cellular automata\, and s
 equential dynamical systems. \nThese models have a long history of use in 
 the biological setting.\nBoolean networks\, for example\, were originally 
 introduced to model genetic regulatory networks.\nMore recent generalizati
 ons of Boolean networks\, _qualitative_ networks\, have allowed experts to
  build and reason about large\, complex models of signaling pathways.\n\n\
 n`GraphDynamicalSystems.jl` provides a common backbone for constructing\, 
 learning\, executing\, and analyzing GDS. As these models are 1) dynamical
  systems\, 2) graphs\, and 3) compositional in their behavior\, they make 
 for a great use case for recombining different packages and ecosystems to 
 create something new—something Julia excels at. In this case\, the packa
 ge hooks into the `JuliaDynamics`\, \n`JuliaGraphs`\, and soon\, the `Alge
 braicJulia`\, `JuliaReach`\, and `SciML` ecosystems. With this package\, w
 e hope to stimulate the implementation and development of new methods for 
 learning and analysis of this broad class of systems.
DTSTAMP:20260502T094003Z
LOCATION:Room 4
SUMMARY:GraphDynamicalSystems.jl: discrete\, finite-state systems over grap
 hs - Reuben Gardos Reid
URL:https://pretalx.com/juliacon-2026/talk/9MGLXG/
END:VEVENT
END:VCALENDAR