Research methods
How I approach research problems.
A research workflow that connects real-world problems, evidence, experimentation, modeling, evaluation, interpretation, validation, and reproducibility.
Real-world problem
Understand the problem before choosing a modelProblem Formulation
Translate a real-world problem into a precise research problem, objective, and measurable outcome.
Data & evidence
Understand what the available evidence can supportExperiment design
Design the investigation before optimizing the modelModel development
Develop an appropriate model for the research problemEfficiency
Balance predictive performance with computational constraintsEvaluation
Determine whether the model actually worksDiagnostic trade-off
Increasing sensitivity can reduce false negatives, while increasing specificity can reduce false positives. The appropriate operating point depends on the consequences of each error.
Interpretation
Understand why the model succeeds or failsReal-world validation
Determine whether the research survives outside the benchmark- Problem
- Data
- Model
- Constraint
- Validation
- Outcome
A useful research result should survive realistic constraints rather than only perform well on a benchmark. The case study connects model behavior to the actual environment in which the method is intended to operate.
Deployment & prototyping
Translate research into a usable systemReproducibility
Make the research transparent and repeatable
Research principle
The goal is not to use the most complex model.
It is to build the most appropriate, measurable, interpretable, efficient, and reproducible solution for the question.