Machine Learning

MNIST Variational Autoencoder
A simple variational autoencoder trained on MNIST. Theory and code included.
MNIST Variational Autoencoder
Clustering Algorithm Demo
This was a class project I made for CS4964 Computational Geometry. It walks through the theory and code behind 4 popular clustering algorithms.
Clustering Algorithm Demo
SOP For Performig BLR in Python
A standard operating procedure for performing Bayesian linear regresssion, in python, from scratch.
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SOP For Performig BLR in Python
A Gaussian Process Classifier Made From Scratch, in PyTorch
This explains how to create a GP Classifier with all the theory and code in PyTorch
A Gaussian Process Classifier Made From Scratch, in PyTorch
MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching
C. Zeng, Harry Winston Sullivan, T. Egg, M. M. Martirossyan, P. Höllmer, J. Jin, R. G. Hennig, A. Roitberg, S. Martiniani, E. B. Tadmor, M. Liu
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Physics-Informed Gaussian Process Inference of Liquid Structure from Scattering Data
Harry Winston Sullivan, Brennon L. Shanks, M. Cervenka, Michael P. Hoepfner
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Experimental Evidence of Quantum Drude Oscillator Behavior in Liquids Revealed with Machine Learning Assisted Iterative Boltzmann Inversion
The first experimental evidence of quantum Drude oscillator behavior in liquids is determined using probabilistic machine …
Brennon L. Shanks, Harry Winston Sullivan, Pavel Jungwirth, Michael P. Hoepfner
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Bayesian Analysis Reveals the Key to Extracting Pair Potentials from Neutron Scattering Data
Learning interaction potentials from the structure factor is frequently seen as impractical due to accuracy constraints of neutron and …
Brennon L. Shanks, Harry Winston Sullivan, Michael P. Hoepfner
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Accelerated Bayesian Inference for Molecular Simulations using Local Gaussian Process Surrogate Models
While Bayesian inference is the gold standard for uncertainty quantification and propagation, its use within physical chemistry …
Brennon L. Shanks, Harry Winston Sullivan, Abdur R. Shazed, Michael P. Hoepfner
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