Projects

Below are some of the projects I have developed throughout my research. Most of these (and others) can also be found on my GitHub.

• Libra-ML. A Python package for predicting the regioselectivity of Rh-catalyzed olefin hydroformylation using 3D structure-based deep learning. From just a ligand SMILES, Libra-ML automatically generates and calculates transition-metal complex geometries and applies a 3D graph neural network to predict selectivity, exposed through both a command-line interface and a Python API for rapid exploration of metal–ligand chemical space.
• Landscaper. A Python framework for constructing, quantifying, and visualizing deep-learning loss landscapes in both low and high dimensions. With efficient sampling strategies, a novel TDA-based metric suite, and interactive visualization tools, Landscaper provides an end-to-end workflow for uncovering geometric and topological structure in modern ML models.
• MolSelector. A lightweight web app for triaging molecular structures (.xyz, .mol, .mol2). Point it to a directory of files, inspect each molecule in an interactive 3D viewer powered by 3Dmol.js, and quickly tag it as accepted or rejected. Decisions are logged to a csv file in the same folder for downstream analysis or version control.