About Me
Michael is a third-year Ph.D. candidate in Statistics and Data Science at Northwestern University and a Research Assistant at the Institute for Policy Research. His research develops statistically rigorous methods for exploratory analysis in hierarchical and multilevel data structures. More specifically, he focuses on advancing unsupervised learning and machine learning techniques tailored to the complexities of multivariate meta-analysis, with the goal of improving the reliability and interpretability of evidence synthesis across diverse empirical domains.
Skills
- Machine Learning
- Meta Analysis
- Exploratory Analysis
- Statistical Modeling and Analysis
- Data Visualization
- Experimental Design
- Unsupervised Learning
Education
-
PhD in Statistics, Expected Spring 2027
Northwestern University
-
MS in Statistics and Data Science
Northwestern University
-
BS in Statistics and Analytics, BA in Economics, Minor in Neuroscience
University of North Carolina at Chapel Hill