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THE BIG FESTIVAL ABOUT SMALL CITIES
Tom Tom champions civic innovation, creativity, and entrepreneurship in America’s hometowns.

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avatar for Daniel Emaasit

Daniel Emaasit

Haystax
Data Scientist
McLean, Virginia
I am a Data Scientist at Haystax in Washington, D.C. My interests involve developing principled probabilistic models for problems where training data are scarce by leveraging knowledge from subject-matter experts and context information. In particular, I am interested in flexible probabilistic machine learning methods, such as Gaussian processes and Dirichlet processes, and data-efficient learning methods such as Bayesian optimization & Model-based Reinforcement Learning.

I am the creator of Pymc-learn, a library for practical probabilistic machine learning in Python.

I am also a Ph.D. Candidate of Transportation Engineering at UNLV where my research in nonparametric Bayesian methods is focused on developing flexible-statistical models for traveler-behavior analytics.


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