Computer Science (CS)
My Journey
I began studying CS as a freshman at UR and quickly discovered that computer science is a beautiful balance of logic, reasoning, and creativity. We can look through our CS lense to solve problems outside of the programming world. Additionally, I love how collaborative this area of study is. At school, I met some of my best friends in my classes and bonded with them staying up till all hours of the night working on our assignments.
ProjectsProfessional Experience
Projects
My Work
Head over to Github to view some of my personal and academic projects!
Some of my favorite projects are:
- Data Science geoFellowship: I created robust algorithms for data production, focusing on estimating road quality from high resolution satellite data using convolutional neural networks.
- NASA Grant Forest Disturbances and Ecosystem Services: I worked with a team to create a Tethys web app that supports research on the Amazon watershed and indigenous territories. Ultimately we want to understand how forest degradation, deforestation, and road building affect the ecosystem services provided by the hydrologic cycle in the Southwestern Amazon.
- Huntington Hill & Webster Apportionments: This Python script outputs the appropriate Huntington-Hill and Webster apportionments given state name and population data. Completed as a final project for our Math & Democracy course.
- UR Italian Studies Translation Lab Website: I created and designed a website using WordPress to publish translations of various works by transnational writers.
- Nucleotide Alignment: This program determines the optimal alignment of two nucleotide sequences. Optimality is determined by the values of the gap penalty g and the weight matrix (that lists all the similarity scores of (ai , bj)). Finally the program outputs the optimal similarity score along with the optimal alignment.
- Kruskals Algorithm: Using the Swing GUI toolkit, this program implements Kruskal’s algorithm to calculate the minimum spanning tree (MST) of a graph.
- MLB Salary Data: This program reads data from https://madisonlondon.github.io/mlbsalarydata.github.io/ and uses pandas, a software library, to organize the data and store it in a DataFrame. The program will iterate through the column of salaries then given the condition that we are working with a syntactically correct monetary value, will store the salary as a float. We find any outliers in the dataset using IQR and then calculate the average.
Professional Experience
My Career
Programming Counselor (June 2018-August 2018)
Software Engineer Intern (May 2019-January 2020)
Computer Vision Intern (May 2020-August 2020), Computer Vision Co-op (August 2020-December 2020)
Commonwealth Cyber Initiative Fellow (October 2020-May 2021)
Associate Full Stack Developer (Augusty 2020-Present)