You Say You Want Transparency and Interpretability?

  We keep hearing and saying that in order to implement and correctly use machine learning and predictive models, they must be transparent and interpretable. That makes sense. You don’t want a black box model making important decisions — although one could argue that the guts and intuitions of many human beings are often as opaque and [...]

By |2018-02-08T03:02:27+00:00April 29th, 2016|BlogPost|0 Comments

What can non-profits learn from social media targeting done by the Obama 2012 Campaign?

[Cross-posted on the Edgeflip blog] We’ve all been there when designing a social media strategy – post something on Facebook or Twitter and see what happens. Oftentimes, this leads to more questions than answers: Who sees the post? Who gets influenced by it? Am I getting the audience I want? Am I extending my audience [...]

By |2018-02-08T03:02:27+00:00September 24th, 2014|BlogPost|0 Comments

One-to-One (Personalized) Public Policy

One of the primary reasons I joined University of Chicago was the chance to be at the intersection of Computation, Data, and Policy, and work on large-scale social problems that lead to an impact on public policy. I’m often asked how Data/Analytics/Machine Learning can practically impact any kind of policy decisions and the analogy I [...]

By |2018-02-08T03:02:27+00:00August 22nd, 2013|BlogPost|0 Comments

Is Data Science a Real Science?

Many people, including myself, often say that if a “science” has the word science in it, it‘s probably not a real science. Computer Science, Political Science, Social Science, Rocket Science…Although there’s probably some truth to that, I don’t think I entirely believe that. So what about Data Science? Well, there is a lot of buzz [...]

By |2018-02-08T03:02:27+00:00July 13th, 2013|BlogPost|0 Comments