COMPUTATIONAL QUANTUM PHYSICIST
ELECTRONIC STRUCTURE OF SUPER ATOMIC CRYSTALS
Using Density Functional Theory to Study 2D Materials
Super atoms are clusters that are larger in size than molecules and smaller than bulk solids in which elemental atoms have covalent bonds with other
PYTHON PACKAGE INTERFACE FOR ELECTRONIC STRUCTURE CALCULATIONS
Using ASE to Drive Electronic Structure Calculations with PySCF
Performing electronic structure calculations can often involve packages that are difficult, at first, to use. This open-source project helps solve this problem by creating an interface between Python-based Simulation Chemistry Framework, or PySCF, Python package and the Atomic Simulations Environment, or ASE. This easy-to-use interface allows everyone from the science enthusiast to the scientist to study electronic structures. Visit the PySCF branch of my ASE-PySCF Interface project to clone and try for yourself.
GRAVITATIONAL WAVE DATA ANALYSIS TUTORIAL
Python 2 and Python 3
This project shows how to complete the Gravitational Wave Data Analysis tutorial that is written in Python 2.x, in Python 3.x. Once LIGO, the Laser Interferometric Gravitational-wave Observatory, detected gravitational waves in 2016, they began to publish their data, giving the public access to explore for themselves. They, alongside Virgo, then created the Gravitational Wave Open Data Analysis Tutorial to guide viewers on this exploration. While Python 2.x is still widely used, Python 3.x is extremely popular, yet is backward incompatible. It is my goal and hope that Python 3 folks enjoy this tutorial to help further their scientific exploration.
INTERACTIVE WATER QUALITY DATA VISUALIZATION
Using NYC Harbor Water Quality Data to Visualize Improvement Trends
New York City harbor water quality data is available on NYC Open Data, but due to the amount available, can be challenging to visualize. Here, I have created a Jamaica Bay Dissolved Oxygen Concentration interactive graph that gives the user an opportunity to view data points, trendlines, and zoom in on sections. This was completed on Jupyter Notebook using python libraries including Numpy, Pandas, Plotly, and Dash.
