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Finding the Why behind Complex Data

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My Background

My Education

My academic background combines human behavior, technology, and data science. I earned a PhD in Applied and Experimental Human Factors Psychology from the University of Central Florida, an MS in Human-Computer Interaction from Georgia Institute of Technology, and a BS in Science, Technology, and Culture from Georgia Institute of Technology. In 2025, I also completed the Data Science and Applied AI Certificate Program at Massachusetts Institute of Technology.

Industries Served

Healthcare & Pharmaceutical Organizations

Health systems, insurers, and pharmaceutical or medical-device companies depend on quantitative analysis to improve clinical outcomes and operational efficiency. My quantitative analysis supports clinical trial statistics, outcomes research, cost-effectiveness studies, and real-world evidence analysis. By modeling patient data and treatment results, we can help organizations identify trends, evaluate the effectiveness of therapies, and optimize healthcare delivery. Advanced analytics also support forecasting patient demand, improving hospital operations, and guiding strategic decisions around new treatments and technologies.

Government, Public Policy & Central Banks

Government agencies, policy institutions, and central banks rely on my quantitative modeling to inform complex economic and regulatory decisions. I can contribute to economic forecasting, policy evaluation, and financial risk analysis. My work often includes stress testing financial systems, analyzing economic trends, and assessing the impact of regulatory or fiscal policies. Through statistical modeling and predictive analytics, we help policymakers simulate potential outcomes and develop data-driven strategies that support economic stability and long-term growth.

Primary Areas of Work

Are You in The Healthcare Sector ?

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    Let BHP create a thriving data-centric approach wherever you are.

    My background in human-centered research informs measurement decisions and interpretation of quantitative results, without overextending beyond what the data support. I prioritize transparent methods over black-box analysis.

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