Crazy Rich Bayesians is the personal blog of Daniel Emaasit. This blog's name is inspired by the title of the Hollywood movie Crazy Rich Asians.Get it...? It rhymes.
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.