The University of Massachusetts Amherst
University of Massachusetts Amherst

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DS4CG 2020 Projects

Data Science for the Common Good 2020 runs from May through August.

 

AMC is one of the leading science-based environmental conservation organizations on the East Coast and has ambitious goals to reduce its carbon footprint. The AMC project focuses on developing and testing new methods of measuring and predicting carbon emissions associated with guest travel and operation of AMC facilities, such as lodges, camps, cabins, and staff quarters. The fellows will develop models and visualizations of guest usage and staff operations, and implement prototype software modules that support decision-making related to energy savings, enabling AMC to both reduce and offset their carbon footprint in a data-driven manner.

 

AuCoDe is a startup company that automatically detects and analyzes online controversies. The AuCoDe team will explore applications of controversy detection technology on misinformation surrounding the COVID-19 pandemic found in public forums. By examining news coverage and public social media discourse using AuCoDe’s controversy detection technology, the DS4CG team will identify signals to detect, track, and understand the dynamics of coronavirus-related misinformation online and better inform the public.

 

 

The Department of Veterans Affairs (VA) operates one of the largest integrated healthcare systems in the United States. While it maintains a large repository of electronic health records, this raw data does not lend itself to analysis and research purposes. The VA project will focus on developing and validating algorithms to automatically extract characteristics and features from the data, such as diseases, treatments, and biomarkers. The project may also involve processing narrative data from physician notes. This work will help the VA generate insights into the treatment and care of patients.

 

 

Associate Professor Eric Poehler of the UMass Classics department has thousands of photographic images of frescoed walls in Pompeii, the ancient city that was buried after the eruption of Mt. Vesuvius nearly 2,000 years ago. Each image has captions describing the objects included and other features. The Pompeii team will develop models to identify objects in the images, and then search images for objects that may not be mentioned in the captions. The team will also work on detecting unlabeled objects in images such as scaffolding or unwanted signage. The project results will vastly increase researchers’ ability to analyze and understand the archeology of Pompeii.