Authored a guidebook for responsibly investing in AI and data science for philanthropic donors and foundations.

Built a taxonomy of Data- and AI-for-Good programs, driving understanding of what is underfunded, overfunded, and where gaps exist in strategic use of data and AI in the social sector. Wrote and published articles on the practical and unique applications of data science and AI to the social sector for nonprofits, the public, and philanthropic funders

* Worked with the blogsearch team to create statistical models for event detection. * Implemented a burst detection algorithm in C++ that processed terabytes worth of data to find statistically significant increases in frequencies of keywords. * Had my work incorporated into the Google codebase for use in other projects. The project was chosen for continued development and is used in blogsearch.google.com.

I'm taking a sabbatical after my time at DataKind to reflect, recharge, and do laundry.

* Designed algorithms for learning statistical models of highly variant patterns in a minimax entropy framework. Applied to hierarchical and contextual representations of objects in images * Created a new MCMC sampling algorithm inspired by Swendsen-Wang clustering for sampling arbitrary posterior distributions over graphical models. Showed improvement over iterated conditional modes, Swendsen-Wang clustering, and graph cuts. * Optimized algorithms to process and extract statistically significant patterns from large datasets in a tractable amount of time. Achieved a 20% reduction in learning time over previous versions of this learning algorithm using a modification of hit-and-run Gibbs sampling. * Responsible for coordinating with a 10 person team in the US and a 20 person team in China to integrate my code into a unified software system in C++. * Created graphical interfaces for relevant experiments in C#, requiring a bridge between managed and unmanaged C++ code. Show less

* Designed and implemented algorithms to process gigabytes worth of network traffic data to identify anomalous behavior (DDOS attacks, zombie machines, etc.). * Used R to visualize and analyze massive network datasets to identify useful features for classification. * Created a realtime event detection system that used exponentially weighted moving averages to identify likely suspicious behavior. * Wrote code for the R programming language to allow it to interface with databases too large to fit into memory (> 2GB). Show less

Headed data science research and development for building prototypes for the 2-3 year future of media. Projects / responsibilities included: Built a realtime social media scraping and analysis pipeline (Project Cascade – http://nytlabs.com/projects/cascade.html) using Python. Project was productized and is forging a new revenue stream for the NYT from external clients. Scaled out a data storage system for all tweets / clicks on NYT content using MongoDB and Hadoop, making Project Cascade scalable to multiple clients. Performed analyses around social data and other NYT data using R to guidestrategic vision of company and unearth insights that were used to inform media strategy. Reports were presented to internal and external clients with a range of data literacy levels. Built a data collection / storage / analysis pipeline for realtime geo tracking data from mobile phones (http://openpaths.cc). Gave regular tours to important advertisers and NYT clients of the R&D lab, explaining ideas clearly and entertainingly to a range of audiences. Show less

Founded and acts as Executive Director of a non-profit that empowers mission-driven organizations to use data science to tackle humanity’s toughest problems. DataKind leads a community of passionate data scientists, visionary partners and mission-driven organizations with the talent, commitment and energy to use data science in the service of humanity. DataKind is headquartered in New York City with Chapters in Bangalore, Dublin, San Francisco, Singapore, the UK and Washington DC.