I am an astrophysicist turned data scientist who enjoys cycling, triathlons and tinkering with electronics.
I received my Ph.D. in Astronomy and Astrophysics from the University of Chicago in 2014, where I worked on studying how large datasets from cosmic microwave background (CMB) experiments and galaxy surveys can constrain fundamental physics. I used numerical computation, data analysis and sophisticated statistical methods such as Bayesian statistics and Monte Carlo techniques. For more details on my research please see research and publications for my scientific work.
I have worked on a number of data science projects, ranging from graph analysis of the New York social elite and sentiment extraction from Yelp reviews using NLP, to developing machine learning models in scikit-learn to predict a new venue’s popularity from available meta-data when the venue opens, e.g., where it is located, the type of food served etc.
I like to solve interesting data and machine learning problems, and to apply these solutions to real world problems.
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Currently reading: Spark: The Definitive Guide by Bill Chambers and Mattei Zaharia.