About DSSI

We are training the next generation of socially responsible data scientists.

The Innovation Accelerator Laboratory for Data Science and Social Impact (DSSI) aims to serve as a national hub where students, researchers and community organizations come together to solve real-world problems with human-centered data science.

History:

Starting in Fall 2023, a group of Harvey Mudd College faculty and staff began building a vision of an Institute for Data Science and Social Impact where our technical and data-science skills could be applied for real social good. Our group grew to 14, including computer scientists, mathematicians, statisticians, operations researchers and climate scientists, guided by external board members, all coalescing around five key pillars. We won initial seed funding though Harvey Mudd’s Innovation Accelerator program in Spring 2025.

DSSI focuses on five key pillars:

• Providing a site for the development of the emergent academic field at the intersection of data science and social impact. The Institute will facilitate two-way knowledge transfer between academics and domain experts to co-create frameworks for the use of data in a social impact contexts.

• Advancing research in mathematics, data science, and related fields, and fostering innovation in their applications to social challenges. Examples of possible areas of focus include natural language processing, predictive modeling, methods for big data, statistical methods, operations research, topological data analysis, and artificial intelligence (AI).

• Creating research-to-action pathways. The institute will build networks with social scientists, activists, and community-based organizations for real world impact and to ensure that our cutting edge data science research is informed and motivated by the people it serves.

• Developing data literacy and technical capacity among all practitioners; in our students, in our community organizations and in the public.

• Education, dissemination, and outreach. The Institute will train the next generation of mathematical scientists via research-involvement and (re)train working academics in high-quality work at the intersection of data science and society. This pillar also supports the creation and dissemination of curricular materials, datasets, and mathematical tools to serve as national resources for mathematics and data science programs at other institutions.