Two student research projects that
are worth paying attention to
At the Cloudera Foundation, we bring technology, expertise and capital to nonprofit organizations that are solving critical problems around the world. We need partners — they bring expertise and local knowledge about their specific missions that we simply don’t have. By combining their depth and skill with our expertise in big data, machine learning and advanced analytics, we’re able to make a bigger impact.
Since partnerships are an essential part of our strategy, we’ve worked hard to find smart, capable organizations that are already making a difference in the world. Unsurprisingly, the nonprofit sector is full of smart, driven people who care deeply about the world. One of the real pleasures in our jobs is working with so many of them!
The geoLab at the College of William & Mary is an excellent example. Dan Runfola, the geoLab’s Principal Investigator, has assembled a team of researchers that brings analytic and machine learning approaches to geographical data, and uses the insights his team gains to support important social missions worldwide. We met the team when AidData became one of the Foundation’s first Data4Change grantees in 2018. That collaboration continues to go well, and our relationship has deepened over time.
In April, I was scheduled to judge a research accelerator competition in which W&M students would pitch their innovative data projects. When COVID-19 shut down college campuses, we took the competition virtual. I invite you to read more about two standout projects from this adapted contest (drumroll…):
- The geoBoundaries project, led by students Syndey Fuhrig, Joshua Pangiban and Sylvia Shea, collects, curates and publishes the geographical outlines of sub-national administrative regions worldwide. This includes cities, counties and other geopolitical areas. The data is widely used; it’s important in disaster response, policy-making and regional planning. The project is fairly mature, and the third major release of the global dataset hit the web site just a week or two ago. You can read more about this excellent work in the blog post that the team wrote up.
- An earlier-stage effort led by recent graduate Ethan Harrison, uses machine learning techniques to predict where, in regions of conflict and strife, displaced people are likely to go. Predicting the destinations of migrants and refugees is important especially in planning for effective response: establishing housing and sanitation, providing food and more. This work touches as well on several important privacy issues: How do we protect migrants from exploitation or worse by using data about them in this way? And more work is certainly necessary to address those issues. But the project is an interesting one, and you can read Ethan’s post here.
If it looks like our partnership work goes much further than the typical foundation-grantee relationship, then we’re doing this right. We’re grateful to both teams, and to the geoLab leadership, for their efforts and our ongoing collaboration!