Trained physicist bringing analytical and problem-solving expertise to
real world challenges in data science and software development
I enjoy tackling fresh challenges, especially when they involve coding, data, math, abstact structures, and technical systems. In my journey through physics, I discovered many ways to optimize my analytical and meticulous mindset to overcome all sorts of interesting problems. I'm always looking for opportunities to apply what I’ve learned to overcome real-world obstacles, making a direct and meaningful impact on both businesses and communities.
I’ve coded in both procedural and object-oriented languages and am familiar with a wide array of software tools. Having experience in computational mathematics, data handling and visualization, and web development, I’ve learned that the key is knowing which tool to use for the situation. So, efficient learning of new tools and skills is the cornerstone of my professional development. In many projects, I performed complex calculations on large numerical datasets and handled and visualized the outputs. I then critically analyzed results to infer meaningful interpretations, thus leading to optimal solutions. My physics background enhanced my innate abilities, as I worked with an abundance of models and processes all requiring unique, logically driven solutions. The problem-solving skills I’ve acquired from physics augment my analytical and technical skills in ways that are beneficial to any group effort.
Tools: Python, Pandas, NumPy, SciPy, Plotly, Flask, Dash, MySQL, MongoDB, HTML, CSS, Bootstrap, JS, C++, C
Talk only goes so far. Let's see some projects!
I developed a web app that helps customers fill contract PDFs. Their recorded information can be checked and modified by an attorney. Once both parties agree to the information and the customer completes payment, the contract PDFs are auto-populated, signed, watermarked, and emailed as flat, uneditable forms.
Tools: Python, Flask, MySQL, HTML, CSS, JS, PDF
I created Python code that autoruns C/C++ code for a user-set varying range of input parameters. Hundreds of outputs are then organized and visualized in tables of graphs showing the effects of changing parameters against one another. This allowed the team to view the effects of altering the input parameters in a visually comprehensible manner. Appropriate input parameters were then easily discovered.
Tools: Python, Matplotlib, os, C/C++
This code calculated HRG Model pressures used as input for simulation code. By cleverly circumventing iteration by performing calculations on arrays the results were more accurate than earlier ones, and a 10x speedup was obtained bringing runtimes from 50+ hours to just a few. This allowed the team to test parameters in a more efficient manner and ultimately get the best input data.
Tools: Python, SciPy, NumPy
Check out my collection of data visualization dashboards. These are web app dashboards displaying statistical and analytical visualizations of various datasets. In each, I investigate the data to find patterns (or lack thereof) that tell a story about the data.
Tools: Python, Pandas, NumPy, Dash, Plotly, HTML, Bootstrap, CSV