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Patrick Chang

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Patrick Chang comes into this program having worked 6 years in consumer research and information technology. As a Senior Product Manager for Nielsen, Patrick oversaw multi-million dollar digital ad-targeting products within the Nielsen Marketing Cloud, the largest digital distributor of first-party online/offline data. Through effective communication and application of technical knowledge, Patrick unified sales and engineering goals, which contributed to a 200% and 150% YOY revenue growth in his first two years respectively. Outside of his main role, Patrick used Nielsen’s data and resources to increase reach to underrepresented communities. After co-founding the Asians in Media Council, he tackled the issues of Asian-American representation in the Nielsen TV Panel by analyzing census data to develop foreign language documentation and an outreach plan. He also served as a volunteer consultant for Techsoup, utilizing Nielsen’s low-income cell phone ownership data to optimize their free cell phone distribution plan.

This interest in applying data to answer difficult problems began at Columbia University, where Patrick studied Industrial Engineering/Operations Research and Sociology. He discovered a passion at the intersection of the two disciplines—applying analytics towards social issues. To explore this growing enthusiasm, Patrick built models for data-driven professors Dr. Yotam Margalit in Political Science and Dr. Luoping Zhang in Public Health, contributing to papers on topics such as the identification of motivating factors for incumbent election voter turnout and the formation of a meta-analysis framework for cross-study collaboration called Systems Biology. Through this research experience, Patrick developed the ability to navigate and translate complex data sets into communicable findings.

Patrick is most interested in learning how to use the power of machine learning and neural networks to help resource-strapped organizations address issues that would otherwise be too work intensive. Through the MSiA program, Patrick hopes to build knowledge of prescriptive, descriptive, and predictive modeling. Armed with his professional and educational experiences, his curiosity for answering social questions, and his MSiA data science toolkit, Patrick looks forward to solving larger infrastructural problems.