I am a computer science student at University of British Columbia and an engineering graduate from the University of Victoria. Currently I am seeking internship opportunities for summer or fall 2024. During my free time I enjoy delving into technical hobbies to improve my abilities in software engineering and electronics. Outside of technical work, I enjoy trying new cuisines, snowboarding, and surfing.

Transparent Media is a chrome extension that I developed that provides information to the user about the political bias reporting accuracy of the news site they are viewing. Currently the extension can provide this information in the form of a popup window when viewing an article or news site. The extension also adds stickers and tooltips indicating political bias to any news site present on a Google search result. These features can be disabled in the settings/options menu. The extension retrieves data from an API I also developed and deployed that is a web scraping service written specifically for scrapping data from www.allsides.com and mediabiasfactcheck.com. The API will periodically scrap the data and store it on a PostgreSQL database to ensure the data persists when the API service is idle during times of no use. Currently this extension is in testing phase but there is hope to upload the extension to the Chrome store for public use once the extension has finished testing.

CipherPad is notepad app with the ability to encrypt text with a personalized key and share encrypted content over messaging or file sharing services. Other users who receive encrypted content can use the app to decrypt and read the content with the correct key. CipherPad provides an effective means for someone to secure the contents of private notes and share encrypted versions of the note to others. This project was developed using Java, SQLite, and the android dev environment for IDEA. An APK of the first release of the application is available for download at the download icon below.

The Automatic Sorting System was the final project assigned for the mechatronics course (MECH458) offered at the University of Victoria. The goal of the project was to design and program a system that can successfully sort 48 objects into 4 different categories in under 60 seconds. The objects were made from one of the following materials: black plastic, white plastic, aluminum, or steel. These objects were categorized based on the material the system detected they were made of. The system consisted of a conveyor belt with laser trip sensors and an IR reflectance sensor, a rotating sorting tray, and an Atmel 8-bit microcontroller (AT90USB1287). The final demo of the project successfully sorted 48 pieces in 38 seconds with 4 errors made.

Melanoma is a dangerous form of skin cancer that is often hard to diagnose early due to its similar looking nature to benign moles. For this project a convolution neural network was developed and trained to help identify suspicious moles that may have melanoma. The model outputs 10 labels which indicate the presence of asymmetry, pigmentation network, regression areas, blue-whitish veil, and 6 colors. The structure of the convolution neural network was optimized to determine a configuration that yielded the most suitable results. The final model configuration consists of 3 convolution layers that have max pooling layers sandwiched in between, this is followed by 3 dense layers. The training of the model used the PH2 data set which consists of 200 dermoscopic photos of moles along with description and diagnosis given by a dermatologist. A modified version of the data set was created by performing horizontal and vertical reflections to produce a data set for validation. Statistical analysis was performed on the model to determine accuracy and reliability of the label classification of the neural network.