Translated by Google
Mathematics for preparatory and higher education: from concept to mastery, by a CentraleSupélec engineer
From 57 C$ /h
Whether you're in a preparatory class for engineering schools (scientific track or business school), a bachelor's degree, an engineering school, or a master's program, higher-level mathematics moves quickly, and a poorly understood concept can hold you back from an entire chapter. This course is designed to help you truly understand, and then master, the material.
Graduate engineer from CentraleSupélec, eligible for the agrégation option probability-statistics (2026), I am preparing a doctorate in AI applied to automation and I have taught more than 400 hours at the university.
Each session is tailored to your needs: questions from the week, practice exams or midterms to prepare for, or a particularly challenging exercise. I begin with intuitive approaches (diagrams, concrete examples, sometimes a Python simulation), then we move on to more rigorous methods: definitions, key proofs, and the writing style expected in competitive exams. You then work through progressively challenging exercises and leave with a summary of the methods.
Areas of expertise: analysis, linear algebra, probability, statistics, financial mathematics and mathematical foundations of machine learning.
Graduate engineer from CentraleSupélec, eligible for the agrégation option probability-statistics (2026), I am preparing a doctorate in AI applied to automation and I have taught more than 400 hours at the university.
Each session is tailored to your needs: questions from the week, practice exams or midterms to prepare for, or a particularly challenging exercise. I begin with intuitive approaches (diagrams, concrete examples, sometimes a Python simulation), then we move on to more rigorous methods: definitions, key proofs, and the writing style expected in competitive exams. You then work through progressively challenging exercises and leave with a summary of the methods.
Areas of expertise: analysis, linear algebra, probability, statistics, financial mathematics and mathematical foundations of machine learning.
Extra information
Send me your course materials (lectures, tutorials, practice tests or past exams) and your learning objectives before the first lesson. Bring a pen and paper, or a tablet, and a stable internet connection. Lessons can be taught in English.
Location
Online from France
About Me
I teach because I love seeing an abstract concept finally become clear. Trained as an engineer, I worked for over ten years in the energy sector, in about ten countries, using mathematics every day to solve real-world problems. I then chose to return to more theoretical and cutting-edge subjects: applied mathematics, AI, and quantitative finance.
I am a demanding but supportive teacher. I always start with intuition before moving towards rigor, and I enjoy curious students who want to understand rather than memorize: students in preparatory classes, undergraduates, engineering schools, or master's programs. Lessons are offered in French or English.
I am a demanding but supportive teacher. I always start with intuition before moving towards rigor, and I enjoy curious students who want to understand rather than memorize: students in preparatory classes, undergraduates, engineering schools, or master's programs. Lessons are offered in French or English.
Education
PhD in AI applied to automation (in progress) – POMDP models applied to electricity markets
Master 2 in Applied Mathematics, specializing in financial mathematics – Aix-Marseille University (currently being finalized)
Eligible for the competitive examination for secondary school teachers specializing in probability and statistics (2026)
Engineering degree – CentraleSupélec
Master of Science in Electrical Engineering (Power Systems) – University of Wollongong, Australia
Bachelor's Degree in Fundamental Physics – Paris-Sud University
Master 2 in Applied Mathematics, specializing in financial mathematics – Aix-Marseille University (currently being finalized)
Eligible for the competitive examination for secondary school teachers specializing in probability and statistics (2026)
Engineering degree – CentraleSupélec
Master of Science in Electrical Engineering (Power Systems) – University of Wollongong, Australia
Bachelor's Degree in Fundamental Physics – Paris-Sud University
Experience / Qualifications
Over 400 hours of teaching at university and in vocational training programs: probability, statistics, AI, machine learning, electronics
Complete design of an AI and machine learning course at the university, supervision of final year projects
Approximately 100 hours of tutoring
Over ten years of engineering experience in renewable energies: modeling electrical grids and wind farms in about ten countries
IEEE publication on convolutional temporal networks
Complete design of an AI and machine learning course at the university, supervision of final year projects
Approximately 100 hours of tutoring
Over ten years of engineering experience in renewable energies: modeling electrical grids and wind farms in about ten countries
IEEE publication on convolutional temporal networks
Age
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
60 minutes
90 minutes
120 minutes
The class is taught in
French
English
Skills
Availability of a typical week
(GMT -05:00)
New York
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
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