Research
My work centers on structure: mathematical structure, statistical structure, computational structure, and the operational systems needed to make those ideas useful in real environments.
Research area
My work in number theory explores structural questions in arithmetic, identities, and analytic behavior with emphasis on explicit forms, modular patterns, and connections to large-scale data-driven problems.
Research area
Topology and geometry provide persistent structural language for the study of shape, continuity, and invariance. These tools offer a rigorous basis for understanding information flow, clustering, and regime changes in multiscale systems.
Research area
Topological data analysis uses persistence and homology to capture global structure in noisy data. This enables more robust feature engineering and interpretable analysis in scientific and economic settings.
Research area
Probability and statistics are central to the design of interpretable models, scalable summaries, and robust inference pipelines. I work on deriving analytical and computational procedures that respect uncertainty and structure.
Research area
Machine learning interests include model design, regime detection, feature engineering, and interpretable modeling for time series and structured data, with an emphasis on scientific validity and robustness.
Research area
I design data ecosystems that support research iteration, reproducibility, and decision-making, from ingestion and wrangling to analytics-ready infrastructure.
Research area
Artificial intelligence work here is concerned with principled systems, explainability, and hybrid methods that combine mathematical structure with practical engineering constraints.
