Teaching

"Everything. Contained in this classroom
is a microcosm of human existence
assembled for you to query and examine and ponder"

From the poem "Did I Miss Anything?" by Tom Wayman

Resources

Numerical analysis notebooks

This is a collection of Jupyter notebooks developed for the course Numerical Analysis (MAST 334 / MATH 354) at Concordia University. The purpose of these notebooks is to illustrate the most important numerical analysis concepts using simple Python examples and complement theoretical lectures.

https://github.com/simone-brugiapaglia/numerical-analysis-notebooks

Upcoming courses

Fall 2026

Numerical Analysis (MAST 334)

In this course, students learn about numerical analysis, an algorithmic approach to finding approximate solutions when exact solutions are impossible or unreasonably complicated. Lying at the intersection of mathematics and computer science, numerical analysis is a key component of computational mathematics. Topics may include analysis of errors involved in computations; floating-point arithmetic; root-finding methods; interpolation theory and function approximation; orthogonal polynomials; numerical integration and quadrature formulas; error analysis of numerical algorithms.

Winter 2027

Numerical Analysis II (MATH 494 / MAST 680 / MAST 833 II)

The course will introduce some of the most widely used numerical methods for solving problems in linear algebra and differential equations. In linear algebra, topics of interest include solving linear systems, computing eigenvalues, and matrix factorizations. On the differential equations side, the course will cover methods for solving ordinary and partial differential equations, including approaches based on finite differences and finite elements. Time permitting, we will also explore topics such as modern approximation theory and physics-informed machine learning. The course will include a programming component, preferably in Python.

Statistical Learning (STAT 380 / MACF 491 / MAST 679 H)

This course is an introduction to statistical learning techniques. Topics covered include: cross validation, regression methods, classification methods, tree-based methods, introduction to neural networks, unsupervised learning.

Past courses

Concordia (since 2019)

2024-25

16. Statistical Learning (STAT 380 H / ACTU 491 / MACF 491 / MAST 679 H) - Winter 2025
15. Numerical Analysis (MAST 334 / MATH 354) - Fall 2024
14. Mathematics of Data Science (STAT 497 / MAST 679 / MAST 881 DS) - Fall 2024

2023-24

13. Data Science Lab (MAST 387) - Winter 2024
12. Numerical Analysis (MAST 334) - Fall 2023

2022-23

11. Statistical Learning (STAT 380 H / ACTU 491 / MACF 491 / MAST 679 H) - Winter 2023
10. Numerical Analysis II (MATH 494 / MAST 661 / MAST 881 II) - Winter 2023

2021-22

9. Statistical Learning (STAT 380 / ACTU 491 / MACF 491 / MAST 679 H) - Winter 2022
8. High-dimensional Probability with Applications to Data Science (STAT 497 / MAST 679 / MAST 881 P) - Winter 2022
7. Numerical Analysis (MAST 334 / MATH 354) - Fall 2021

2020-21

6. Topics in Applied Mathematics: Sparsity and Compressed Sensing (MAST 680 / MAST 837 / MATH 494) - Winter 2021
5. Introduction to Statistical Programming (STAT 280) - Fall 2020
4. Numerical Analysis (MAST 334 / MATH 354) - Fall 2020

2019-20

3. Applied Advanced Calculus (ENGR 233 R ) - Winter 2020
2. Applied Advanced Calculus (ENGR 233 U) - Winter 2020
1. Numerical Analysis (MAST 334 / MATH 354) - Fall 2019

Simon Fraser University (2016-19)

2018-19

5. Introduction to Ordinary Differential Equations (MATH 310) - Summer 2019
4. Vector Calculus (MATH 252) - Spring 2019

2017-18

3. Algebra I: Linear Algebra (MATH 240) - Spring 2018
2. Applied Linear Algebra (MATH 232) - Fall 2017 (with Prof. L. Stacho)

2016-17

1. Applied Linear Algebra (MATH 232) - Spring 2017

Minicourses

1. An invitation to high-dimensional approximation: from sparse polynomials to deep learning (3-hour minicourse)
2022 CMS Summer Meeting. St. John's, NL, Canada. June 3, 2022.

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