# Featured Projects

## Self-Assigning Spectra with Deep Learning

Assigning molecular spectra is a necessary evil: we obtain a wealth of information about chemistry and physics through tedious and error-prone manual analysis by searching for patterns and assigning spectral features to transitions between quantum mechanical states. I developed a set of recurrent neural network models with reinforcement learning to “teach” a computer how to solve spectra.

## Teaching Neural Networks

I created a github repository of Jupyter notebooks and environment that teaches the basics of deep learning: starting from low-level implemtations with numpy and jax, and building up to abstractions with PyTorch and beyond. Experiments can be run locally, or in the cloud with Google Colab.

github

## Probabilistic Molecule Identification

Identifying molecules from unknown mixtures is difficult—I developed a stack of probabilistic deep learning models that help transform experimental spectroscopic information into features that can help uncover the identity of new molecules.

journal arxiv github

## Unsupervised Molecule Discovery in Astrochemistry

Predicting what molecule to look for next is a difficult thing to do when chemical and physical conditions of interstellar environments can potentially be highly complex. In this project, I build a machine learning pipeline for predicting molecular properties by leveraging cheminformatics and data science tools to predict and direct astronomical observations and laboratory experiments.

github