CS
I am a recent graduate from San Diego State University
that received a Bachelor of Science in Computer Science.
I am looking for an entry level software development position
where I can grow and improve my knowledge as a newfound
computer scientist. Some of my interests include traveling,
surfing, attending concerts, and rock climbing.
I am personable and a great communicator who is eager to lead. I take pride in my work
and strive to be the best at anything I do. I take constructive
criticism well and am always looking to learn. I think outside
of the box and always try to establish a great connection with
the other members on my team. I can and will be a great asset
to your company.
Me (on the left)
August 2023 - June 2024
June 2023 - September 2023
January 2022 - July 2022
This website enables users to share their favorite hangout spots. To post a new location, users are prompted to provide a fun name,
photos, directions, and tags describing various attributes such as parking, water bodies, accessibility for the disabled, dog friendliness,
type of view, cost, and more. This information helps users prepare for their visits. Once posted, other users can view the location and save it to their
favorites. The number of saves is tracked and publicly displayed to indicate the location's popularity. After visiting, users can leave a review with a
rating out of 5 stars and comments on their experience.
The website uses MongoDB for data storage and is built with HTML, CSS, JavaScript, and React. It
incorporates Express.js and Node.js for navigation and package management. Adding and searching for locations is
facilitated through a connection to the Google Maps API, which simplifies the acquisition of location coordinates for data storage.
With mass shootings being something of concern in the U.S. nowadays,
two peers and I decided to create an application that utilizes
computer vision to recognize a person in possession of a firearm and identify
the class of firearm at the same time. This system would be embedded into security
cameras and would alert on-site security or local police in the event
that a possible shooter is identified. This could minimize or prevent damages caused
by said shooter.
The system utilizes a supervised learning model built with a convolutional neural network.
This model was trained using a dataset of thousands of labeled images,
enabling it to accurately differentiate between various firearm types, such as handguns and rifles.
This application manages pizzeria orders and assigns them to various delivery services.
All orders pass through a buffer that tracks available slots for food items. To promote pizzas
over sandwiches, the pizzeria limits the number of sandwich orders in the buffer at any given time.
Additionally, the total number of active orders is capped to ensure the pizzeria can manage them efficiently.
Multiple delivery services are on standby to accept and transport orders, and all transactions are logged to verify completed deliveries.
The multi-threaded system is built entirely in C++ and employs semaphores and a queue for the bounded
buffer and barrier. All shared data is protected by locks, ensuring mutually exclusive access.