Aileen Nielsen has built an unusually varied career, moving between physics research labs, corporate law, and a series of New York City tech startups. She currently works at an early-stage NYC startup in the time series and neural networks space, applying machine learning to hard, real-world modeling problems. Earlier, she worked at the mobile health platform One Drop and on Hillary Clinton’s presidential campaign, experience that taught her to deliver results under demanding constraints.

Her interests stretch from defensive software engineering and UX design for reducing cognitive load to the interplay between law and technology. She brings many of these threads together in her book Practical Fairness, a guide to building machine learning systems that are fair, robust, and trustworthy, and she is a frequent speaker at machine learning conferences on both technical and sociological topics.

Aileen also serves as chair of the New York City Bar Association’s Science and Law committee, which examines how the latest developments in science and computing should be regulated and how they should inform existing legal practice. She holds an A.B. from Princeton University and is A.B.D. in Applied Physics at Columbia University.