I am a Data Scientist and Machine Learning Engineer who also delves into the worlds of Data Engineering and Web Development. Currently, I am a Lead Data Scientist at NBC Universal, where I focus on creating scalable end-to-end machine learning models for Recommender Systems.
I graduated from the University of California, Los Angeles with a BS in Astrophysics. During that time, I completed four years of research as part of a research fellowship and was also a co author in a paper (details below).
Skills and Experience
For Machine Learning Engineering, some of my responsibilities have included:
(2015-Now)
Timeline
Here are a few notable professional events that include Astrophysics research, and experience in Machine Learning/Data Engineering.
Contact Me(2015-2018)
Conducted image processing and statistical analysis on data take by space telescopes WISE and SPITZER on gas filaments AFGL-5376 and Double Helix Nebula in the Galactic Center.
(Summer 2016)
Used clustering techniques to classify fast radio transients. This work was done under the mentorship of Dr. Casey Law and Dr. Carl Heiles.
(2016-2017)
Collected over three terabytes of observational data from the 100m Green Bank telescope to identify possible technosignatures near TRAPPIST-1.
(2017-now)
Leading the data science team toward researching, developing & experimenting machine learning models to solve problems in Natural Language Processing, personalized UI/UX, and business-oriented analysis.
(2019-now)
Research, prototype, and deploy machine learning models on cloud services to personalize product recommendations as part of an end-to-end machine learning workflow. Furthermore, created a webapp internal tool to be able to observe recommendations from deployed models with their respective metadata.