teaching
Courses in pure mathematics, applied mathematics, data science, and machine learning at undergraduate and graduate levels.
I teach courses spanning pure mathematics, applied mathematics, statistics, and data science / machine learning, at both undergraduate and graduate levels — across IMSP (Benin) and the AIMS network (South Africa, Senegal, Rwanda), plus short workshops and intensive bootcamps for working professionals.
Teaching philosophy
Mathematics is best learned by doing. My teaching reflects that: rigorous theory paired with hands-on projects, where students prove a fixed-point theorem one week and build a TDA pipeline in Python the next. Theory and practice end up checking each other.
I emphasize active learning — problem sessions, coding labs, collaborative projects — over passive lectures. The other half of the job is mentorship: helping students figure out what research question is theirs, building the mathematical maturity to answer it, and making the path from student to contributor as concrete as I can.
Recommended learning paths
Pure Mathematics track: Linear Algebra → Real Analysis → General Topology → Algebraic Topology → Fixed Point Theory → TDA
Applied Mathematics track: Probability → Statistics → Stochastic Processes → Time Series → Bayesian Statistics → Quantitative Finance
Data Science & ML track: Programming → Intro to Data Science → Machine Learning → Deep Learning → NLP / Geometric DL / Reinforcement Learning → MLOps
Each arrow represents a suggested prerequisite. Students can enter at any point matching their background.
Institutions
- IMSP — Institut de Mathématiques et de Sciences Physiques, Dangbo, Benin
- AIMS South Africa — African Institute for Mathematical Sciences, Cape Town
- AIMS Senegal — African Institute for Mathematical Sciences, Mbour
- AIMS Rwanda — African Institute for Mathematical Sciences, Kigali
Courses at AIMS — African Institute for Mathematical Sciences
Selected courses taught at AIMS centres (Rwanda, Senegal, South Africa). Each course has a dedicated interactive Jupyter Book with lecture notes, exercises, and code.
Python programming for scientists
An introduction to Python for scientific computing and data science. Variables, data structures, flow control, functions, NumPy, Matplotlib. Hands-on labs and programming challenges.
AIMS South Africa AIMS Senegal AIMS Rwanda
Jupyter BookExperimental mathematics with SageMath
Computational problem-solving through experimentation. Discrete mathematics, number theory, linear algebra, graph theory, combinatorics. Building SageMath notebooks for mathematical exploration.
AIMS South Africa
Course siteOrdinary differential equations
Existence and uniqueness theorems, first and second order equations, systems of ODEs, stability analysis, Laplace transforms. Applications to population dynamics and epidemiological models.
AIMS Senegal AIMS Rwanda
Course materialsTopological data analysis
Persistent homology, simplicial complexes, Vietoris-Rips filtrations, Mapper algorithm, stability theorems. Implementation with GUDHI and Ripser in Python. Applications to health and financial data.
AIMS South Africa AIMS Senegal
Course materialsNumerical methods with Python
Root finding, interpolation, numerical integration, linear systems, ODE solvers. Error analysis and convergence. All methods implemented from scratch and with NumPy/SciPy, with Jupyter notebooks.
AIMS Senegal AIMS Rwanda
Course materialsWorkshops & short courses (3-5 days)
Existing Workshops
- Workshop on Computational Topology & Quantum Computing (WoComToQC) — Organizer & lecturer
- Data Science Africa — Machine learning tutorials for African researchers
- Python for Mathematical Research — Hands-on computing for mathematicians
- Introduction to TDA with GUDHI & Ripser — Persistent homology in practice
- The Shape of Data — Book-based workshop on geometry-driven ML and data analysis in R
Applied AI & industry
Introduction to generative AI & LLMs
3 days — Prompt engineering, fine-tuning, Retrieval-Augmented Generation (RAG), deployment. Hands-on with OpenAI API and open-source models.
More on the workshopData science for decision-makers
3 days — Non-technical training for managers and executives: understanding AI, identifying use cases, steering data projects, evaluating ROI.
More on the workshopMLOps in practice
4 days — From notebook to production: Docker, CI/CD pipelines, model monitoring, experiment tracking (MLflow), versioning (DVC).
More on the workshopMathematical Foundations of Modern AI
5 days — The mathematical structures behind modern models: linear algebra and network architectures, calculus and gradient-based learning, probability and diffusion models, inductive bias, evaluation and generalization. For professionals who use AI and want to understand what is happening under the hood. No advanced math required.
More on the workshopMathematics & research
Reinforcement learning: from theory to practice
5 days — MDPs, Q-learning, DQN, policy gradients, actor-critic methods. Applications in resource allocation, game playing, and optimization.
More on the workshopGeometric Deep Learning
4 days — Graph neural networks, learning on manifolds, equivariant architectures. Applications in molecular science, social networks, and point clouds.
More on the workshopApplied Bayesian statistics
4 days — Bayesian modelling, MCMC, Stan/PyMC, hierarchical models. Applications in health, finance, and social science.
More on the workshopFoundational skills
Python for data science
5 days — From zero to analysis: Pandas, data visualization, cleaning, exploratory analysis, and first ML models with scikit-learn.
More on the workshopR for statistical analysis
4 days — Tidyverse, ggplot2, statistical modelling, reproducible reports with R Markdown. Companion to The Shape of Data.
More on the workshopScientific writing with LaTeX, Overleaf & Prism
3 days — Writing articles, theses, and dissertations with LaTeX. Collaborative editing on Overleaf and AI-assisted scientific writing with OpenAI Prism.
More on the workshopIntensive bootcamps (8–12 weeks)
Cohort programs run through AIRINA Labs for working professionals and graduate students who want an intensive path to deployable ML systems, rather than a semester-long course. Open to corporate cohorts (banks, telecoms, insurance, public sector) and individual enrolment.
Machine Learning & AI Bootcamp — An experiential approach
10 weeks full-time or 20 weeks part-time. Ten modules from Python through classical ML, deep learning, NLP, LLMs and generative AI, MLOps — closing with a capstone project on a real dataset and a deployable portfolio piece.
Modules: Python · Introduction to ML · Classical ML (classification, regression, clustering) · Recommender systems · NLP · ANN/CNN/RNN · LLMs & generative AI · MLOps & deployment · Capstone project · Portfolio
For: working professionals moving into ML/AI roles, upper-undergraduate and masters students looking for an intensive route, and corporate teams running an internal upskilling cohort.
Format: 100% online, synchronous. Live cohort sessions, hands-on Python labs, weekly office hours, capstone reviewed by working ML practitioners.
More on the workshop Inquire about a cohortLooking for the full catalogue of 38 courses with lecture notes, exercises, and code? The materials are bilingual (FR + EN) and filterable by domain and level.
Browse all 38 courses