About
I’m a computer science student at Las Positas College, preparing to transfer to a four-year university. With a passion for software development, I focus on projects that resonate with me, pouring 100% effort into every task. Why give anything less? I am self-motivated and driven by a commitment to personal growth and fulfillment. My approach combines persistence, curiosity, and a genuine desire to build meaningful, impactful software. I’m constantly learning and challenging myself, knowing that each project brings me one step closer to my goals.
Projects
01. Surface Area of 3d Objects
- Developed a Python graphing tool for calculating arc lengths and surface areas of 3D objects, capable of processing 5 unique mathematical functions, including trigonometric functions, exponential, absolute value, etc.
- Improved runtime of similar online graphing calculators by 1,531.25%, achieving calculations in 0.166 seconds for complex functions like f(x) = x^x (shown left), cross-referenced with geogebra.com's calculator.
- Implemented use of numpy, matplotlib, scipy and sympy for standard calculations (derivatives, and numerical integration), as well as plotting.
- Documentation & research paper explaining the process in developing this found in the README.md in Github repository.
View on GitHub
02. Arbitrage
- Created a Python tool to uncover betting opportunities, analyzing sports odds across various platforms, identifying guaranteed profit margins of up to 7% in 10% of cases.
- Utilized a third-party API for up-to-date odds information, processing data from up to 10 platforms per game.
- Showcased expertise in data processing, analyzing over x odds entries per session to identify mismatches in an average 0.677 seconds.
- Managed data consistency issues, refining the data input process to maintain accuracy in finding profitable outcomes.
- Focused on automating the analysis process, ensuring a seamless evaluation of betting scenarios.
View on GitHub
03. MarineSaver
- Developed an endangered marine species detection tool using a Raspberry Pi with a LoveRPI camera and Keras neural network for image classification, identifying endangered species in fishing catch data.
- Designed an AI-powered solution positioned above conveyor belts to monitor bycatch, enhancing transparency and accountability in the fishing industry.
- Utilized Keras and ImageAI for robust image classification to ensure endangered species were detected accurately, supporting consumer trust in dolphin-safe and eco-friendly labels.
- Addressed environmental conservation by targeting harmful fishing practices, providing a technological alternative to ineffective policies and existing satellite monitoring efforts.
- Calculated cost-effective manufacturing and pricing to make the tool accessible, with a projected profit margin, offering a practical, scalable solution for widespread industry adoption.
View on GitHub
