Cofounder and Chief Data Scientist at Feature Labs.
I’m into Zen Buddhism, urban farming, data science & machine learning, pole vaulting, yoga, mobility/health/fitness, music, skiing, and generally making a fool of myself through art.
Feature Labs is a venture-backed startup enabling enterprises to deploy predictive analytics solutions in a smart, fast, and disciplined way. We develop automation and UI tools so that business executives, subject-matter experts, data scientists, and software engineers can focus on solving business problems instead of computation and academic problems.
I’m one of the lead developers of Featuretools, the most popular open-source feature engineering library on Github. I’ve worked on Featuretools since April 2016; read here why in September 2017 we decided to release it publicly. Featuretools makes it easy to use feature engineering (both manual and automated) in your data science pipelines by abstracting away low-level computations that have become boiler plate in data science/machine learning applications. We’ve found it increases productivity by at least an order of magnitude, makes for more repeatable and deployable code, and frees up cognitive load on developers so they can focus on high-level problems. It uses the Deep Feature Synthesis (academic paper and blogpost) algorithm to automatically generate and compute high-level features from granular, time-varying, multitable data.
Should building AI products require you to “call in the cavalry” – No Read how to engineer data-driven AI products – “Machine Learning 2.0”
Projects we’ve done that we can talk about
- Featuretools - the most popular open-source automated feature engineering framework on Github. Backed by DARPA in the D3M program
- The AI Project Manager - a collaboration with Accenture that used ML 2.0 techniques that produced an augmentation tool for software project managers to accurately predict whether projects meet their goals.
- Helping BBVA predict credit card fraud - automated feature engineering and problem-specific optimizations drastically reduce financial implications of fraud.
My research at MIT focused on data science automation, and specifically, on automatically generated and solving meaningful prediction problems given arbitrary relational data. This work culminated in my master’s thesis as well as a paper in IEEE/ACM DSAA 2016 titled What would a data scientist ask? Automatically formulating and solving prediction problems.
I also helped develop an end-to-end data science foundry for MOOCs that enabled a systematic way of going from raw data to predictive models. This work was published in IEEE/ACM DSAA 2015 (link).
More fun projects include an implementation of near-optimal online matrix-completion algorithms (which had previously only existed in math form), multiplexed robotic scheduling for liquid-handling robots, recipe modification using recurrent neural networks, and scene detection using a combination of convolutional and recurrent neural networks.
Current Projects and Experiments
Pole vaulting ever higher
As always, I am trying to jump a little bit higher every day.
My first foray into urban farming
Everyone should be able to grow their own food. I’m learning how to do this myself, by building a Personal Food Computer and attempting to automate as much of the growing process as possible. My first crop is Purple Bok Choy!
Past Projects and Experiments
One day I will actually write descriptions for all of these!
Human-generated rap is so 20th century. Who’s to say Lil Neuron can’t be the illest rapper alive without being “alive”?
Predicting Body and Mental States
Attempting to predict how tired I’ll feel, how much energy I’ll have, how sick I’ll be, and the locations and times I do things using both passive phone and watch data as well as active recording of the food I eat both as it enters and exits my body and the way I feel throughout the day.