Technical Skills, Data Interpretation
Highlights
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Highlights *
1) Designed and optimized 4+ molecular models of biochemical systems (~200,000+ atoms each), generating and analyzing over 1 million data points; findings that directly supported a $2.5 million National Institute of Health (NIH) research grant.
2) Analyzed 10+ GB of molecular simulation data, identifying trends that agree with structural biology data by over 80%; used Python, Machine Learning, and High-Performance Computing (HPC) resources to uncover insights.
3) Developed technical expertise in the following software and programming languages
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VMD, ChimeraX, PyMOL, GROMACS, AMBER, NAMD, PLUMED
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Python, Bash, Tcl/Tk, MATLAB, High-Performance Computing (HPC)
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Data Science Libraries: NumPy, SciPy, Pandas
Machine Learning Libraries: TensorFlow, Scikit-Learn, PyTorch
Data Visualization: Matplotlib, Seaborn, Grace, GNUPlot
Other skills: Document review/editing, Scientific/Technical writing, Literature search/gathering, PowerPoint Slide Deck development, Adobe Illustrator, LaTeX
Here I present an overview of computational models I have built, data I have collected, and code I have developed for data generation and analysis.
To promote data transparency in research, any additional code, files, and data will be made available free of charge upon request.
Thanks to technological advances, computational tools can be used to construct microscopic models of a chemical system that you are interested in studying, in greater detail. Here are some models I have built.
As small as
These were constructed using a code I wrote in bash
These were less than 1MB of data storage
Or as big as
These were constructed using a code I wrote in Tcl
These were at least 30 MB each
The data collection and processing I have done looks like this
From this
This short video is really 10000 numerical data points, roughly 1.3GB large
(Video generated in VMD Version 1.9.3)
To this
