Good Data, Good Values

Location: King's Suite
Average rating: ****.
(4.47, 19 ratings)

We’ve all heard the opening line before: We’re in the midst of a data revolution, a time in which those with the right skills can harness streams of information to yield insights and create value. Yet for all of our machine learning algorithms and big data tools, so many of the problems we solve day-to-day are decidedly “first world”: figuring out how to get the biggest ROI on ad dollars or crafting personalized movie recommendations. Can we use our skills as data scientists to solve social problems as well, helping people find clean water as easily as they can find good restaurants? How do we draw insight from big (and not-so-big) data to give perspective to social changemakers who don’t have the resources to hire a full-time data science team? How will the influx of publicly available government, foundation, and third party data shape the kind of problems we tackle and the communities we connect with? If we truly are in the midst of the data revolution, let’s make sure we’re playing the part of the heroes.

Photo of Jake Porway

Jake Porway


Jake Porway is a machine learning and technology enthusiast who loves nothing more than seeing good values in data. He is the founder and executive director of DataKind, an organization that brings together leading data scientists with high impact social organizations to better collect, analyze, and visualize data in the service of humanity. Jake was most recently the data scientist in the New York Times R&D lab and remains an active member of the data science community, bringing his technical experience from his past work with groups like NASA, DARPA, Google, and Bell Labs to bear on the social sector. Jake’s work has been featured in leading academic journals and conferences (PAMI, ICCV), the Guardian, the Stanford Social Innovation Review, and he has been honored as a 2011 PopTech Social Innovation Fellow and a 2012 National Geographic Emerging Explorer. He holds a B.S. in Computer Science from Columbia University and his M.S. and Ph.D. in Statistics from UCLA.


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