Mathematics and Statistics Education
Course design, assessment strategy, and evidence-based instructional practices in quantitative disciplines.
Research
Interests
Probability and Data Science
Random graphs, network analysis, graph-based frameworks for exploratory data analysis.
Algebraic Combinatorics
Integer partitions, MacMahon operators, generating series approaches to modeling discrete structures.
Graph Theory
Generalized Johnson graphs and their cliques.
Courses
Courses
Current
SS 2141
Summer 2026
Applied Probability and Statistics for Engineers
Students will learn how to visualize and analyse continuous and categorical data using modern data science tools. Concepts of distributions, sampling, estimation, confidence intervals, experimental design, inference, and correlation will be introduced in a practical, data-driven way.
Open course pageSS 2857A
Summer 2026
Probability & Statistics I
Probability axioms, conditional probability, Bayes' theorem. Random variables motivated by real data and examples. Parametric univariate models as data reduction and description strategies. Multivariate distributions, expectation and variance. Likelihood function will be defined and exploited as a means of estimating parameters in certain simple situations.
Open course pageRecent
DS 1000
Winter 2026, Fall 2025
Data Science Concepts
This course introduces students to foundational concepts in data science, focusing on
the visualization and analysis of both continuous and categorical data. Concepts covered
include data visualization, summary statistics, regression, categorical data analysis,
probability, central limit theorem, confidence intervals and experimental design.
Emphasis is placed on practical, data-driven examples to develop independent
problem-solving skills and connect theoretical concepts to meaningful analysis through
Python.
STAT 230
Winter 2025
Probability
Introductory probability: sample spaces, independence, conditional probability, Bayes' Theorem, and named distributions (Binomial, Poisson, Normal, etc.). Covers random variables, joint/marginal/conditional distributions, means, variances, covariances, and the Central Limit Theorem.
MATH 239
Spring 2025
Introduction to Combinatorics
Introduction to graph theory: colourings, matchings, connectivity, planarity. Introduction to combinatorial analysis: generating series, recurrence relations, binary strings, plane trees.
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Teaching Dossier
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