Faculty Recruiting Support CICS

Data Science for the Common Good (DS4CG) 2022 Poster Session

27 Sep
Tuesday, 09/27/2022 11:00am to 1:00pm
Computer Science Building, Room 150/151
Special Event

Data Science for the Common Good (DS4CG) is a summer program that trains aspiring data scientists to work on real-world problems that benefit the common good. Join the 2022 cohort of DS4CG fellows for lunch and a poster session where you will get to learn about each project directly from students.

Please RSVP so that we make sure to order enough food. RSVP requested but not required. 


DS4CG 2022 Projects


MassChallenge is a non-profit startup accelerator with over 10 years of experience connecting innovative entrepreneurs with community partnerships and financial resources. In this partnership with MassChallenge, students create models to predict applicant success and provide feedback to the entrepreneurs in order to improve their odds of a successful application. Another goal of the project is to develop a recommendation model that provides a "dating app-style" mentor-mentee matching for startups registered in a MassChallenge program.

Media Ecosystems Analysis Group

The Media Ecosystems Analysis Group performs quantitative media research across digital spaces, and supports multiple non-profits to leverage media insights. Students on this team are developing a pipeline for hate speech detection in videos using text transcribed from YouTube, which has become notorious for harboring online hate speech and harmful content. Integrating state-of-the-art natural language processing and information retrieval techniques, they will design an annotation process and a classifier model pipeline for categorizing videos for anti-immigrant extreme speech with highly interpretable, interactive dashboards.

UMass Rescue Lab

UMass Rescue Lab is the premier computer science research group focused on rescuing children from internet-based victimization. Students on this team will create a web application that helps parents protect their children from technology-driven exploitation. The project uses application reviews to identify potential dangers to children in the forms of exploitation and abuse through state-of-the-art NLP techniques and interactive data visualizations.

Data Profiling for Fairness

Algorithmic fairness is one of the most important topics in the last decade of data science, and has attracted significant attention from industry and academia. Students on this team will investigate potential issues in data-driven systems that contribute to algorithmic fairness, in order to help data practitioners understand and identify these issues in their data and the related machine learning tasks. 

Red Cross

Timely reliable damage assessment of buildings and infrastructure in the wake of natural disasters is crucial to enable governments to make emergency declarations, and organize response and recovery efforts. To aid this effort, student on this project leverage modern machine learning techniques to rapidly analyze the before and after satellite images of affected areas to assess damage. Together with the Red Cross, the goal is to build a robust tool that is adaptable to detecting buildings and inflicted damage across the globe.

Herring Project

Accurate and efficient stock assessment methods of commercially relevant fish species are extremely important toward sustainable fisheries management. Currently used manual techniques are highly inefficient, time-consuming, and not incredibly accurate. Students in this group will partner with the MIT Sea Grant group to automate the detection and counting of herring fish species in image and video data for efficient fishery management. The goal is to build an end-to-end platform that takes video inputs, applies state-of-the-art computer vision techniques, and outputs count of herring fishes moving upstream. 

Scholarship America

Scholarship America manages the scholarship process by mediating between donors and recipients. Students on this project will apply machine learning methods within supervised learning to answer questions on graduation outcomes of scholarship recipients.