Scientific Python Ecosystem

• Henry Fredrick Schreiner (Princeton University)

We will cover techniques and common concerns in making fast scientific Python code. The topics we expect to cover: * Using numpy effectively * Using numba to get machine level performance from Python * Using a tensor library like JAX * Basics of multithreading

Scientific Python Ecosystem

• Henry Fredrick Schreiner (Princeton University)

In this section we will cover developing a Python Package. * Environment setup and handling * Package configuration with PEP 621 and hatchling * Using pytest effectively * Static checks * Setting up Continuous Integration

Analysis Grand Challenge / HEP Scientific Python Ecosystem

• Lindsey Gray (Fermi National Accelerator Lab. (US))
• Mason Proffitt (University of Washington (US))
• Henry Fredrick Schreiner (Princeton University)

Analysis Grand Challenge / HEP Scientific Python Ecosystem

• Lindsey Gray (Fermi National Accelerator Lab. (US))
• Henry Fredrick Schreiner (Princeton University)
• Mason Proffitt (University of Washington (US))

Theory of Neural Networks

• Christopher Tunnell (Rice University)

Convolutional Neural Networks

• Christopher Tunnell (Rice University)

Probabalistic Reconstruction with Bayesian networks

• Christina Peters (University of Delaware)

pyhf: pure-python HistFactory for hypothesis testing and limit setting

• Giordon Holtsberg Stark (University of California,Santa Cruz (US))
• Matthew Feickert (University of Wisconsin Madison (US))

Make a WIMP Limit in Pure Python

• Christopher Tunnell (Rice University)
• Amy Roberts (University of Colorado Denver)