EDUCATION
University of Arizona | Bachelor of Science in Computer Science | GPA: 4.0 Expected May 2027
- Relevant Coursework: Computer Programming I & II, Discrete Math I, Data Structures & Algorithms (Udemy)
TECHNICAL SKILLS
- Languages: Python, Dart, JavaScript
- Frameworks: Django, Flutter
EXPERIENCE
Web Developer: Python, PHP, HTML/CSS, JavaScript Dec 2023 – Present
Science Mentorship Institute, (Non-profit organization providing mentorship for the underrepresented in tech) Remote
- Updated information on the organization’s website, increasing user engagement and credibility.
- Contributed to issues related to the login portal and Home Page, resulting in a better user experience for the mentorship program
Student Worker & Research Assistant: Python, ArcGIS Pro, API Feb 2024 – Present
Vertically Integrated Project, University of Arizona Tucson, AZ
- Utilizing ArcGIS Pro to extract and process large spatial datasets, resulting in the identification of key trends and patterns in soil survey data
- Conducted comprehensive data analysis, collecting and analyzing over 12,000 data items to quantify heavy wave and drought events and corresponding crop yield data, revealing significant correlations and informing decision-making
- Training and testing a machine learning model using datasets from six US Southwest states, planning to achieve an accuracy of 85% in predicting crop yield responses to environmental stressors
PROJECTS
Writer Bot (Beginner’s ChatGPT): Python Mar 2024 – Apr 2024
Personal Project
- Developed a Python program generating random English text based on the statistical properties of an original text, using Markov Chain Analysis to determine word probabilities, resulting in a 90% accuracy rate in replicating the original text’s coherence and readability
- Built a table of over 50000 prefixes and suffixes from the original text, allowing for the generation of new text that replicates the original’s statistical properties, making it potentially readable and coherent, with an average of 3.2 suffixes per prefix
- Implemented a text generation algorithm that selects suffixes based on their diversity, ensuring that the generated text maintains the original’s statistical properties, and printed the output in a formatted list of 10 words per line, generating over 100 lines of text in under 2 seconds