Statistical Consulting
Statistics you can defend.
I’m a PhD statistician and Associate Professor who helps researchers, teams, and organizations design studies properly, model data honestly, and deliver results that hold up to scrutiny — from the first power calculation to the final dashboard.
What clients get
A defensible answer, not just a number: the right design for the question, methods chosen for the data rather than habit, uncertainty stated plainly, and a writeup or interactive tool your stakeholders can actually use.
study design DOE mixed models reliability AI evaluation R / Shiny
Credentials
Grounded in peer-reviewed work.
Training & position
PhD in Statistics (Simon Fraser University, 2018). Associate Professor, Department of Mathematics & Statistics, University of the Fraser Valley.
Published methods
Peer-reviewed research in experimental design and applied statistics, with methods independently cited across textile engineering, food science, hydrology, and mining research.
Accreditation
Member of the Statistical Society of Canada; P.Stat. (Professional Statistician) application under review.
What I help with
Three services, one standard.
Study & experiment design
Sample size and power, randomization, factorial and fractional-factorial designs, including the baseline parameterization — so the data you collect can actually answer your question.
Modelling & analysis
Mixed-effects and generalized linear models, measurement reliability, calibration and validation. Documented, reproducible, reviewable.
Tools & dashboards
Interactive R / Shiny applications that put your model in front of the people who need to act on it — see live examples.
Statistics for the AI era
Your AI is only as good as its evaluation.
Benchmark scores drift with the test harness, top-of-leaderboard gaps sit inside statistical noise, and most evaluation pipelines are never checked for reliability. Before an AI number drives a decision — which model to buy, whether a system is ready to ship — someone should ask whether the measurement itself can be trusted. That’s measurement science, and it’s what I do.
Evaluation design & audit
Design AI evaluations that answer the question — proper sampling, contamination control, uncertainty on every comparison — or audit the one you have.
Reliability of judgments
Human raters and LLM-as-judge pipelines are measurement instruments. I quantify their dependability and tell you how many judgments a trustworthy verdict needs.
Experiments that respect your budget
When every evaluation run costs money, designed experiments get you more answer per dollar than one-at-a-time testing of prompts, configurations, and models.
Beyond consulting
Research, software, and teaching.
Projects
Deployed forecasting apps and open-source software — working demonstrations of the tools I build. Explore →
Research
Published work in experimental design and applied statistics, with an active research program in measurement reliability. Publications →
Blog
Notes on statistical methodology — how to design, measure, and trust the numbers. All posts →