Smit Lunagariya
Smit Lunagariya is a Machine Learning Engineer at Google and an active contributor to the scientific Python and open-source ecosystems. He holds an Integrated Dual Degree (Bachelor's and Master's) in Mathematics and Computing Engineering from the Indian Institute of Technology (BHU), Varanasi.
His involvement in open source began with Google Summer of Code in 2020 and has since expanded to include contributions to several scientific computing projects, including SciPy, SymPy, LPython, LFortran, QuantEcon, and Aesara.
Session
This tutorial demonstrates how to accelerate agent-based simulations using modern Python tools. Using Thomas Schelling's classic segregation model as a running example, participants will learn to transform readable but slow Python code into high-performance implementations using NumPy and JAX. The tutorial explores how mild individual preferences can lead to extreme aggregate outcomes through simulation, while teaching practical techniques for leveraging modern hardware (including GPUs) to make realistic large-scale simulations computationally feasible. Participants will gain hands-on experience with performance optimization strategies applicable to economic modeling, urban planning, epidemiology, and other domains requiring large-scale agent-based simulations.
Installation Instructions: https://github.com/QuantEcon/scipy_tutorial_2026