|role:||Machine Learning Subject Lead|
|time period:||since Jun 2016|
data science • Linux • Python • numpy • scipy • matplotlib • ipython • scikit-learn • PostgreSQL • NoSQL • Kyoto Cabinet • LevelDB
As part of Flowminder’s humanitarian work in a conflict zone in the middle east, I extracted descriptive statistics and created geospatial visualizations from de-identified call data records provided by an operator of a mobile phone network.
This project posed some great challenges around infrastructure. The core data source, falling within the terabyte range, needed to be prepared for large-scale statistical analysis in a computationally efficient manner and integrated with a heterogeneous set of supplemental data sources for contextualization.
As part of my exploratory analysis, I extracted descriptive statistics and created geospatial visualizations of various characteristics of the data.
The project was conducted through a multidisciplinary team involving data scientists as well as subject matter experts. There was a strong culture of contextualizing results through subject matter expertise to get to actionable insight.
In addition to my role within the project team on this project, I was Flowminder’s point person to focus on machine learning aspects of the work going on throughout the organisation.