Analyzing Energy Usage with Predictive Modeling

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Partner: Massachusetts Division of Capital Asset Management & Maintenance DS4CG 2023. DCAMM is responsible for managing resources in various state buildings like state hospitals, prisons, universities, community colleges, office buildings. This project analyzed 5 year energy usage of 279 utility meters in 23 academic buildings. Using this data, time-series prediction models were developed for 12-month energy consumption of various utilities (electricity, steam, natural gas, water) by building. Prediction is the first step towards data-driven efficient management of energy resources and energy conservation.

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Analysis of #StopAsianHate on Twitter

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Partner: Co-Insights DS4CG 2023. This project takes a longitudinal approach to analyzing the #StopAsianHate hashtag on Twitter in order to understand changes in hashtag usage. We developed a model that converts text into embeddings, clusters the embeddings into groups, and links the similar groups to reveal data about context surrounding #StopAsianHate. By analyzing the main accounts driving conversation and identifying transitions in hashtags to discussions, we can better understand social media discussion mechanisms. 

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Tracking Bird Migration Patterns with Machine Learning

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Partner: Environment Canada DS4CG 2023. Aerial insectivore populations have been declining in Canada, prompting the need for a model that can predict bird migration patterns. When a roost of birds take off together, they appear as a distinct shape in weather radar data. By adapting algorithms to fit Canadian weather radar data, we created a model that accurately predicts migration patterns. In addition, the model can even detect roosts that were previously missed by EC. 

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Integrating DISCount for Disaster Relief

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Partner: Red Cross Netherlands DS4CG 2023. The focus of the second summer of our partnership with the Red Cross was to integrate DISCount, UMass Computer Vision Lab's new approach to estimating counts in object detection, into the Red Cross' workflow for counting damaged buildings after a disaster. Our lightweight model helps responders save time and effort in determining the disaster's impact and severity, which ultimately helps the Red Cross deliver aid quickly.

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Red Cross: Satellite Imagery for Disaster Assessment

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Timely reliable damage assessment of buildings and infrastructure in the wake of natural disasters is crucial for organized response and recovery efforts. To aid this effort, this project leverages modern machine learning techniques to rapidly analyze the before and after satellite images of affected areas to assess damage.

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