(Some of) What We've Done


Andrew Means developed and launched the Do Good Data conference. In just 4 years the conference grew from 125 participants to over 800 from more than 20 countries around the world. He then partnered with Stanford Social Innovation Review to merge with the Data on Purpose conference. Following on that success he worked with them to develop the Digital Impact World Tour, a 10 city, 6 continent tour that provoked discussion on the risks and rewards of digital data in civil society.

Most recently, Andrew has launched Good Tech Fest. A three day festival celebrating the opportunities that data and technology provide to create social impact.

Kicking off Do Good Data 2016.

Kicking off Do Good Data 2016.

Fellowship Programs

During his time at Uptake.org, Andrew developed the Uptake.org Data Fellows program. The program consistently received nearly 150 applications from a dozen countries around the world for it's data science track. In addition to data science it developed a track for data security as well. 

These kind of informal learning communities are vital to providing training and capacity building in the nonprofit sector. The connections made between the fellows, their mentors, and one another have outlasted the formal program.


Application Development

While at Uptake.org, Andrew oversaw the development of three applications. The first, Student Union, uses machine learning to help first-generation and low-income students identify colleges where they are more likely to succeed. Within months of launch Student Union already had 1,000 users from across the country utilizing the tool.

The second, ReRoute, helped anti-trafficking groups in India & Nepal share information and better identify trafficking as it was occurring at the border. The project faced immense challenges due to the remoteness and technical capacity of our partners but the team developed of a suite of mobile and online tools that gave groups better insight into their data.

Finally, AutoFocus helped conservation groups and researchers automatically tag their camera trap data using machine learning algorithms. The team developed a host of classification algorithms that helped identify empty images, whether humans were present or not, and even identify particular species.

Our partners collecting data at a border crossing in Nepal.

Our partners collecting data at a border crossing in Nepal.