Airbus Ship Detection

In this project, we aim to explore the application of deep learning techniques such as Convolutional Neural Networks (CNN), to highlight an innovative approach to automatic ship detection in the context of maritime domain awareness. The paper aims to provide a brief description of the problem,...

Relevium

Relevium can identify disaster cycles as a medium for identifying the hazard risk and consequently prepare a community for effective mitigation. Vulnerable zones, their types, effects, and the depth of a disaster are identified. This can be used for developing an early-warning communication...

Gradient Boosting Implementation from Scratch

Inspired by the sustained popularity of the Gradient Boosting Regression Tree (“GBTR”) algorithm, we go back to square one in an attempt to complete a from-scratch implementation in Python. This paper documents a journey from the basics of the regression tree algorithm through gradient boosting...

Analysis of IPMUS NHIS Survey Data for Covid-19

In the United States, there have been over 47 million cases of COVID since the pandemic started in early March of 2020. Based on socioeconomic factors, certain groups of people have been affected by the pandemic more than others. In order to take a closer look at the disparities between...

Audio Digits Classification

In this project, we focus on a small subset of this field, namely, the task of audio digit recognition and classification with the goal of comparing novel convolutional neural network approaches to the more traditional statistical learning methods.