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Applied Mathematician | Modeling , Optimization, and Scientific Python
Applied Mathematician | Modeling , Optimization, and Scientific Python
I translate ambiguous problem statements into mathematical models and run optimization and sensitivity analysis in Python to support technical decisions.
Interested in Applied Scientist and technical R&D roles in the Netherlands, focused on modeling, optimization, and scientific Python.
Selected Projects
Selected Projects
BunnyRAG: graph-aware retrieval and evaluation workflow
BunnyRAG: graph-aware retrieval and evaluation workflow
Developed a graph-aware retrieval method for cross-domain coverage, designed multiple synthetic benchmark regimes to evaluate robustness, and refactored the prototype into a more reproducible Python workflow.
Demonstrates: experiment design, robustness testing, trade-off analysis, reproducibility, codebase hygiene.
Inverse Lithography: manufacturable mask optimization
Inverse Lithography: manufacturable mask optimization
Proposed a lower-dimensional, manufacturable parameterization for inverse lithography; helped shape the formulation and interpretation of results during a TU/e–ASML modeling project; translated between domain and modeling language.
Demonstrates: mathematical modeling, constrained optimization, trade-off analysis, technical mentoring
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