Name: Matthew Heffernan
Type: User
Company: McGill University
Bio: AI Verification Engineer | Theoretical nuclear physics PhD | Programming in Python | Bayesian methods and large-scale computing
Twitter: mrhheffernan
Location: San Francisco, CA
Blog: mrhheffernan.github.io
Matthew Heffernan's Projects
Resources for Introduction to Bayes Workshops
Selected worked examples from Andrew Gelman's Bayesian Data Analysis 3rd Edition
A Burger's Equation Modeling Project
A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
Stripped down version of "https://github.com/Duke-QCD/hic-eventgen" used to generate equation of state matching lattice to a hadron resonance gas
Erdos project development directory
Particlization model for relativistic heavy-ion collisions
Exploring Generative Adversarial Networks
This program computes the particle pair HBT correlation from Monte-Carlo samples of emitted particles
This code can read in a freeze out surface from 2+1D or 3+1D viscous hydro codes (cpu-vh, gpu-vh) or viscous anisotropic hydro codes (cpu-vah) and calculate 3D particle spectra. It can perform numerical integration to find the smooth particle spectra, or perform sampling for a particle list. It is accelerated via OpenMP and CUDA.
Driving time maps are everywhere. But what if you want to optimize driving time to two locations?
Bayesian parameter estimation for relativistic heavy-ion collisions
Sandbox for Julia projects
Exploring LLMs from documentation and toy models to more complex use cases
This repository is part of a "Machine Learning from Scratch" seminar at Harvard Medical School.
Brief examples of autologging in mflow to demonstrate ease of use for bayesian applications in physics
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
This is the official code repository for MUSIC, a (3+1)D hydrodynamic code package for relativistic heavy-ion collisions
In-house dictionary for new graduate students
Materials for Physics 8805 at Ohio State University: "Learning from data: Bayesian methods and (some) machine learning"
Python example scripts and useful functions
Personal heatmaps for GPS data from Strava, Garmin Connect, and Polar.
UrQMD tailored for use as a hadronic afterburner