Experience

 
 
 
 
 
March 2017 – Present
New York

Manager, Data Science

Capital One

ELI5: I lead a team studying how we can use machine learning fairly, to improve customer service and experience, and change banking for good.

Technical Lead for Fair and Explainable AI Research Group within Card Machine Learning. Compliance analytics for fair lending, natural language processing for customer service analytics, customer segmentation.

 
 
 
 
 
September 2013 – January 2017
Cambridge, Massachusetts

Research Scientist

MIT CSAIL

ELI5: I started and ran a research lab to prove that the Julia programming language was useful for big data and data science work.

Started and managed the Julia Lab together with Professor Alan Edelman, providing the main academic funding responsible for the development, growth and adoption of the Julia programming language. Applied Julia to problems in high performance computing, computational genomics, and statistical computing. The lab comprised 16 students and postdocs at its peak.

 
 
 
 
 
June 2013 – September 2013
Kusatsu City, Shiga Prefecture, Japan

Visiting Scholar

Ritsumeikan University

ELI5: I coded up a model for studying how drug molecules dissolve in water, and shipped it in commercial software.

Productionized and shipped the 1D- and 3D-RISM (Reduced Interaction Site Model) codes that are now available in Accelrys Discovery Studio.

 
 
 
 
 
June 2009 – May 2013
Cambridge, Massachusetts

Postdoctoral associate

MIT Chemistry

ELI5: I made new computer models of how molecules trap light and turn them into electricity. I used these models to study new materials used for OLEDs and solar cells.

Computational chemistry research on organic semiconductors, using new techniques of random matrix theory blended with new molecular models for describing atomic charge excitations and transfer.

 
 
 
 
 
May 2004 – October 2002
Singapore

Member of Technical Staff

DSO National Laboratories

ELI5: I made new materials that would distort light and/or blow up, and shoot them with lasers to see how they would react.

Synthesized and characterized novel materials for nonlinear optics and energetic materials (explosives), with organic and inorganic chemical synthesis techniques and nonlinear laser spectroscopy.

Selected Publications

Assessing the fairness of a decision making system with respect to a protected class, such as gender or race, is challenging when class membership labels are unavailable. Probabilistic models for predicting the protected class based on observable proxies, such as surname and geolocation for race, are sometimes used to impute these missing labels for compliance assessments. Empirically, these methods are observed to exaggerate disparities, but the reason why is unknown. In this paper, we decompose the biases in estimating outcome disparity via threshold-based imputation into multiple interpretable bias sources, allowing us to explain when over- or underestimation occurs. We also propose an alternative weighted estimator that uses soft classification, and show that its bias arises simply from the conditional covariance of the outcome with the true class membership. Finally, we illustrate our results with numerical simulations and a public dataset of mortgage applications, using geolocation as a proxy for race. We confirm that the bias of threshold-based imputation is generally upward, but its magnitude varies strongly with the threshold chosen. Our new weighted estimator tends to have a negative bias that is much simpler to analyze and reason about.
FAT* ‘19 Proceedings of the First ACM Conference on the Fairness, Accountability, and Transparency of Algorithmic Systems, 2018

The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in credit decisioning. These challenges complicate the use of model machine learning and artificial intelligence methods in business decision processes.
FATREC’18 Proceedings of the Second Workshop on Responsible Recommendation, 2018

Recent Publications

Assessing the fairness of a decision making system with respect to a protected class, such as gender or race, is challenging when class …

The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in …

Recent & Upcoming Talks

My publicly available slides and posters are on Slideshare.

Recent Posts

I’m hiring a team at Capital One in New York for R&D efforts to bring state-of-the-art machine learning techniques to the …

Projects

Julia

The Julia programming language

Contact

Skills

Julia

Python

git

github

machine learning

data science

regulatory compliance

natural language processing

Docker

microservices

Amazon Web Services

continuous integration

test-driven development

open source