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UID:pretalx-scipy-2026-RQQDGA@pretalx.com
DTSTART;TZID=CST:20260714T133000
DTEND;TZID=CST:20260714T173000
DESCRIPTION:This tutorial demonstrates how to accelerate agent-based simula
 tions using modern Python tools. Using Thomas Schelling's classic segregat
 ion model as a running example\, participants will learn to transform read
 able but slow Python code into high-performance implementations using NumP
 y and JAX. The tutorial explores how mild individual preferences can lead 
 to extreme aggregate outcomes through simulation\, while teaching practica
 l techniques for leveraging modern hardware (including GPUs) to make reali
 stic large-scale simulations computationally feasible. Participants will g
 ain hands-on experience with performance optimization strategies applicabl
 e to economic modeling\, urban planning\, epidemiology\, and other domains
  requiring large-scale agent-based simulations.\n\nInstallation Instructio
 ns: https://github.com/QuantEcon/scipy_tutorial_2026
DTSTAMP:20260715T023004Z
LOCATION:Accelerated Computing
SUMMARY:Computational Methods for Simulation using JAX and NumPy (Room HSEC
  2-110) - Smit Lunagariya
URL:https://pretalx.com/scipy-2026/talk/RQQDGA/
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