Machine Learning

Sound-based Detection of Detection of COVID-19 using Machine Learning

In the year of 2021, even with the development of a COVID-19 vaccine, the emergence of COVID variants have solidified the disease as a consistent presence in the foreseeable future. Current widespread testing methods for the virus are slow, expensive, or require specialized equipment to yield accurate results, causing uncertainty and spread while results are processed, and also disproportionately impact low resource communities that do not have access to equipment needed. This project sought to find a machine learning framework for COVID-19 classification using cough sounds only, providing instant, low cost, high accuracy test results and eliminate or significantly reduce the problems in existing methods.

Learning Panoramic View Synthesis

A synthetic data generation and evaluation pipeline for panoramic computer vision models. This system extracts image and depth data from 3D datasets and outputs rendered comparisons. The evaluation pipeline gives researchers the ability to generate highly accurate, novel snapshots from multiple 3D environment datasets, lowering the resource cost of experimentation.

Learning to Denoise Low-Dose CT Scans

This SURP project explores the use of deep learning to denoise raw CT image data acquired at low-radiation dose. The goal is to recover clean CT images from noisy data that are comparable to CT scans imaged at full-radiation dose.

Shark Spotting with Drones

Shark Spotting with Drones is a research project sponsored by the California Polytechnic State University, San Luis Obispo College of Engineering. Our project focuses on implementing a trained neural network to recognize and learn about sharks and other marine life in an effort to make the ocean safer for everyone.

Coronavirus Update and Resources

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