Technical Projects

This service gets a patient signed into the database, sends the information to the room where the doctor will meet the patient, then wait until the right doctor or professional shows up to take care of the issue, using facial recognition to confirm that the patient's private information is not leaked to any passerby. We used Python to connect the Firebase database to our HTML website, using the code to confirm a patient's identity with the doctor's database, then sending the confirmed information to the doctor and using Python's openCV to set up the facial recognition. Then we allow the HTML site to display the patient's medical records while allowing them to be changed and updated as necessary.

Developed for the BioMedical Engineering Society's second annual BioHack hackathon competion.

Check it out here!

A web app that uses computer vision and Google Cloud Platform's Firebase database to visually identify the ideal makeup for any user's skin color and type.

Developed for UCR's Annual Rose Hack 2020 hackathon competition. This project placed third at the competition.

Check it out here!

Prototype model of a device built to be placed in the body's vasculature to detect angina, or narrowing of blood vessels from cholesterol or coronary disease. Using ultrasound, the device finds where the blood vessel is highly constricted, past a healthy level, which provides the surgeons a precise location for stent placement.

This project placed first at a BioMedical Engineering Society + American Medical Student Association medical hackathon competition.

Check it out here!

Bacterial vaginosis (BV) is one of the most common vaginal infections that ovulating women encounter. In affected individuals, there is an imbalance of the beneficial and harmful bacteria; however, the occurrence of this imbalance cannot be attributed to one specific reason. Additionally, the diagnostic tools needed to aid women are not accessible for the majority of people with the condition. Our device, COHLD, implements a combination of gold standard diagnostic tools along with a novel enzyme-based fluorometric test, to provide a rapid, cheap, and accessible diagnosis of bacterial vaginosis.

Developed for UCR's Bioengineering Senior Design course.

For more information on this project, please contact me!

A deep learning neural network using the 3D U-Net architecture for the segmentation of the substantia nigra. The degradation of the substantia nigra causes Parkinson's disease, so this network is useful to aid in the diagnosis and detection of the disease. Further development will enable the network to fully perform a diagnosis, without the need for a clinician.

Developed for the UCR Honors Program Senior Capstone project. Presented at the 2021 Undergraduate Research Symposium.

Check it out here!

Our app takes on a unique approach finding vitamin D sufficiency by informing users how much sunlight they need. When it comes to finding the right amount of sunlight, there are a variety of variables that go into making sure an individual can be vitamin D sufficient. From skin pigmentation to season, there is no “one size fits all” to this solution. Hence why, our app narrows down specific details, such as location, season, age and skin pigmentation, from each individual that will help provide a response tailored to their sunlight needs.

Developed for the BioMedical Engineering Society's third BioHack hackathon competition. This project won the award of Best Startup at the competition.

Check it out here!

As the communication barrier between the Hearing and Deaf communities continues to grow, I Am Hear is a much needed translator for American Sign Language (ASL). By signing the letters of the ASL, the deep learning neural network, AlexNet, is able to classify and output the images taken from the webcam as text.

Developed for UCR's Annual Rose Hack 2022 hackathon competition. This project placed first at the competition.

Check it out here!