Henry Wu

I’m a computational and applied mathematics student at Carnegie Mellon University, with an additional major in Statistics and Machine Learning. I combine rigorous quantitative thinking with practical data work to turn messy information into decisions.

In my Data Analyst Intern role at Nielsen in Guangzhou, China, I supported market and competitor research through desk research, tracked key data metrics, and helped prepare regular business reports. I worked closely with data and research agencies to deliver timely insights for business questions and trends, and I contributed to analytical projects across a range of topics.

My capstone project with Optum focused on U.S. national drug overdose death rate trends from 2019–2022. I used feature selection to identify influential predictors, quantified effects, and ran sensitivity analysis on missing data to test robustness. I also benchmarked machine learning models against traditional linear regression to evaluate predictive performance and interpretability trade-offs.

I further strengthen my engineering foundation through software work and teaching. On the Carnegie Mellon Racing autonomous team, I designed OOP architectures in Python, built data pipelines for cross-functional teams, and managed GitHub workflows with testing and code reviews. As a teaching assistant for Algorithm and Advanced Data Structures, and as a peer tutor/SI leader, I coached others with tailored learning materials and inclusive group sessions.