Backed by an engineering PhD, I provide high-quality scientific writing and train research teams and students to use AI critically without compromising rigor, accuracy, or scientific standards.
A scientist or project owner operating outside your formal training, needing to rapidly learn, evaluate, and choose methods without compromising rigor
A technical or innovation team pushing an ambitious idea forward and struggling to translate feasibility, accuracy, and timelines into something defensible
A researcher or team lead facing conflicting data, unclear assumptions, or results that “don’t add up”
A manager who knows something is wrong in a manuscript, analysis, or model, but needs someone who can reason through uncertainty, not just polish text
A team navigating high-stakes work where method choice, statistical reasoning, or conceptual framing could quietly determine success or failure
Most students and research teams I support already have strong ideas, technical skill, and meaningful results. What slows progress is not a lack of ability, but unclear methodological framing, conflicting assumptions, and limited guidance on how to express complex reasoning coherently. Without a stable reasoning structure, the work feels heavier than it should, alignment erodes, and forward momentum stalls.
Manuscripts, grants, slide decks structured for review-ready clarity, rigor, and conciseness.
Train teams to verify AI outputs, catch subtle errors, and reason rigorously under scale.
Clear and concise articulation of complex scientific reasoning aligned with supervisor expectations
Deep scientific literacy paired with fast, self-directed problem solving
Ability to reconstruct and validate analyses under severe data constraints
Calm, structured reasoning when results are inconsistent or unclear
Experience correcting high-stakes scientific work without compromising rigor
Clear thinking that restores evidence-based scientific clarity and moves work forward efficiently
“Working with Ri brought immediate clarity to a manuscript we were stuck on. Faced with conflicting statistical results and no access to raw data, she independently reconstructed the analyses, corrected the majority of inconsistencies, and recommended a more appropriate statistical approach. What initially felt unsalvageable was resolved quickly and thoroughly.”
Ri Xu, PhD provides evidence-based scientific writing and AI-assisted workflow support for science-led organizations and technical teams. Her work strengthens scientific rigor, reasoning accuracy, and clarity in fast-moving environments where small errors or poorly used AI tools can undermine good science.
Based in Montreal and working internationally, Ri partners with scientists, lab leaders, and technical professionals to translate complex work into clear, defensible communication and efficient, rigor-first workflows. Backed by an engineering PhD and over four years of experience, she helps teams use AI critically without compromising scientific standards.