Consultant
Toronto, ON
Independent research and analytical work spanning completed empirical studies, research-software platforms, computational engineering, and technical reporting. What follows describes methods and outputs; it makes no claim about clients, budgets, or commercial deployment.
Structures broad research questions into workable analytical plans by separating assumptions, data needs, and testable methods, then builds the pipelines that answer them and writes up what the evidence will and will not support.
- Methods
- Python, MATLAB, C++20, CUDA, OpenMP, CMake, PyTorch, SQL, configuration-driven experiments, automated testing, statistical inference, time-series analysis, signal processing, machine learning, LaTeX technical reporting
- Outcome
- Evaluates assumptions, inputs, and acceptance criteria so technical outputs can be interpreted rather than treated as black boxes, and translates technical uncertainty into concise notes and next-step options.
- Outputs
- Tested research pipelines, empirical reports, prototypes and research platforms, reproducible figures and tables, benchmark and sensitivity analyses, manifests, technical notes, and documented limitations.