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Fall 2021 Capstone Projects

Learn about Texas A&M University Department of Computer Science and Engineering capstone projects completed in fall 2021 and see our sponsors.

Company Sponsors

Our fall 2021 company sponsors for capstone projects were:

Projects

View of webpage made by team American Visualization that shows a list recent flights and destinations visitors to the website might also enjoy.

American Airlines Visualization

A team of five designed and developed a web app that recommends flight destinations to users based on their flight history. Research area: AI, ML, NLP

3D renderings of multicolored circles, squares and a cone.

BRL-CAD Managing Material Metadata

Material data in BRL-CAD is currently just material name and density. For their project, this capstone team was tasked with finding a way to represent material data to run accurate geometric simulations and render military vehicles with material specific information and shaders. Research area: Graphics, Visualization and Computational Fabrication.

Overview of Team MABL's capstone project with an image of a small green four-wheeled remote vehicle. A Texas A&M University logo is in the bottom middle. Text in image: MABL Domain Adaptation. Lauren King, Patrick Maedgen, Austin Burkhalter, David Acosta. Our goal - which we successfully reached - is improving data-detection through training on various dataset and ultimately improving domain adaptation. This was achieved through training on a dataset involving base images, augmented images, and salienced images. We completed the project by showing a live demo of running the trained model on the jetson nano.

Department of Defense Multi-Transfer Learning

For its project, a team of four students used machine learning to improve domain adaptation. Research area: AI, ML, NLP.

The general design approach of the Item Matcher algorithm laid out in a chart.

VARIS Item Matcher

The system takes a large input of products with their unique characteristics and clusters identical items. Companies can use this system to classify products with little human intervention. The team used clustering algorithms and language processing techniques like the Bag of Words model. Research area: AI, ML, NLP

General design approach of the DRHM Matchmaker algorithm.

VARIS The Matchmaker: Product Matching Algorithm for E-Commerce

For its project, a senior capstone team matched identical products across a massive dataset of more than 70,000 items using machine learning. Research area: AI, ML, NLP.

Headshots of members of team Dana: Dani Hott, Amanda Latham, Nate Krall, and Austin Nutt.

VARIS VAPER (Varis Application for Product Entity Resolution)

A team of four worked with Varis, an e-commerce industry sponsor, to develop an algorithm that could parse through a large data set in order to eliminate duplicate entries and match products within that data set. This project used clustering algorithms, machine learning and natural language processing. Research area: AI, ML, NLP