Paguidame
I am a PhD candidate in Statistics at the Université de Montréal (since 2024). After a science-track high school diploma, a BSc in Mathematics, and a Master’s in Statistics–Probability at Sorbonne University-Paris (2023), I have been tutoring since 2019 in mathematics and physics–chemistry for middle/high school, and mathematics/statistics at the undergraduate level. My research focuses on Bayesian causal inference and missing data—so I work daily with probability, linear algebra, optimization, and modeling. I live in mathematics and I enjoy making it clear, concrete, and doable.
My teaching style is simple and rigorous. We begin with a short diagnostic based on your notes, assignments, and recent tests: I identify fragile concepts, missing automatisms, and how you currently approach problems. Then we set 2–3 near-term, measurable goals. We revisit the key ideas without overload—usable definitions, contrasting examples, and a mini-proof only when it truly helps. Then comes guided practice and targeted exercises so that you explain the method yourself, reuse it independently, and gain confidence. The tone is calm, precise, patient.
Tools and materials. I use a digital whiteboard, Desmos/GeoGebra for geometry and calculus, compact method sheets, and banks of graded exercises. For statistics and probability I work in R or SPSS mainly, with step-by-step visualizations and commented solutions. I alternate classic textbook problems with small “real-life” tasks (measurement, rates, mixtures, experimental uncertainty). When a formula feels intimidating, we rebuild it from a simple example so you see what it means and why it works.
Topics covered (examples, not exhaustive).
• Middle/High school: arithmetic and algebra, equations/systems, functions, geometry and trigonometry, derivatives, sequences, probability, statistics, exam prep (Brevet/Bac or equivalent). Physics–chemistry: kinematics, electricity, optics, units and dimensional analysis, experimental method.
• University / prep / CÉGEP: calculus (derivatives, integrals, series), linear algebra, probability, estimation and testing, linear regression, introductory GLMs, hands-on statistics in R.
How lessons run. Online by preference (screen sharing + whiteboard), with the option of brief asynchronous check-ins between sessions (quick question, short correction). Each lesson ends with a one-page “takeaway” and 2–3 consolidation exercises. For longer projects (exam prep, a tough course), we structure a clear study plan and track progress.
My goal is not merely to “do exercises,” but to build habits: read the problem efficiently, sketch or structure the data, choose a method, check the result. Grades usually follow when the method becomes clear and anxiety drops. It’s practical and it lasts.
Parents and students: if you want demanding yet supportive guidance—structured, tailored to your learning profile, and delivered by someone who practices mathematics at a research level—get in touch. We will set a plan, move forward step by step, and keep mathematics on the side of clear ideas.
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