Graduate engineering researcher and technical problem-solver working across operations research, statistical experimentation, forecasting, reliability-oriented decision support, and reproducible Python analytics.
Condition-informed FMEA decision-support prototype using NASA C-MAPSS benchmark data, grouped engine validation, temporal leakage controls, and event-level risk evaluation.
Technical focus: predictive risk · FMEA integration · grouped validation · leakage control · event-level metrics
Scenario-based MILP case study for facility selection and shipment allocation using Pyomo and HiGHS, with independent enumeration-based validation.
Technical focus: MILP · facility location · network optimization · solver validation · sensitivity analysis
Synthetic statistical experiment demonstrating inference, confidence intervals, power analysis, minimum detectable effects, and practical-effect interpretation.
Technical focus: experimental design · statistical inference · confidence intervals · power analysis
Seasonal forecasting demonstration using rolling-origin validation and transparent baseline comparison on a small time-series example.
Technical focus: forecasting · rolling-origin evaluation · baseline comparison · reproducibility
- Programming and analytics: Python, pandas, NumPy, scikit-learn, Pyomo
- Statistics and modeling: A/B testing, confidence intervals, power analysis, forecasting, machine learning
- Operations and engineering: operations research, MILP, facility location, FMEA, condition monitoring, risk analysis
- Validation: rolling-origin validation, grouped cross-validation, temporal leakage control, solver validation, sensitivity analysis, reproducibility
I emphasize transparent assumptions, reproducible workflows, independent validation, and visible limitations. Each featured repository documents what was built, how it was evaluated, and where its conclusions should remain scoped.