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Fields of Specialization Mathematics Statistics Dean Peter Berg Faculty of Mathematics and Science Associate Dean Cheryl McCormick Faculty of Mathematics and Science Core Faculty Professors S. Ejaz Ahmed, Stephen Anco, Chantal Buteau, Hichem Ben-El-Mechaiekh, Henryk Fuks, Omar Kihel, Yuanlin Li, Alexander Odesskii, Jan Vrbik, Thomas Wolf, Xiaojian Xu Assistant Professor Tianyu Guan, Dongchen Li, William Marshall, Pouria Ramazi Participating Faculty Adjunct Professor Vladimir Sokolov (Landau Institute) Professors Emeriti Howard Bell, Mei Ling Huang, Ronald Kerman, Eric Muller Graduate Program Director Sheridan Houghten (Computer Science) Graduate Administrative Coordinator Elena Genkin 905-688-5550, extension 3115 Mackenzie Chown D473 Administrative Assistant Jessica Morrissette 905-688-5550, extension 3300 Mackenzie Chown J415 brocku.ca/mathematics-science/mathematics/graduate-programs/ |
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The MSc program aims to provide students with an intensive advanced education in areas of Mathematics and Statistics in preparation for doctoral studies or the job market. Students will choose a specialization in either Mathematics or Statistics. The MSc program is designed to normally be completed in six terms or twenty-four months. However, completion in twelve months is possible in Statistics specialization. The Mathematics specialization provides students with advanced training in areas of active research and current applicability in algebra and number theory, computer algebra algorithms, discrete mathematics and graph theory, dynamical systems, partial differential equations, functional analysis, mathematical music theory, mathematics education, mathematical physics, solitons and integrable systems, topology, and (as a bridge with the Statistics specialization) probability theory and stochastic processes. The Statistics specialization provides students with solid training in both advanced and applied statistical areas including design of experiment, optimal design for regression, sampling theory, parametric and nonparametric statistical inferences, multivariate statistics, survival analysis and risk models, robust methods, and computational statistics. The program offers two options: a thesis option (intended normally for students planning to pursue doctoral studies) and a project option (intended normally for those planning to join the job market). The paper for the project option will be based on a research project of a practical nature and must demonstrate a capacity for synthesis and understanding of concepts and techniques related to a specific topic. Each student will consult with their Supervisor when planning a program of study and choosing courses and must receive approval from the Graduate Program Director. |
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Participating faculty are engaged in active research in the following areas of specialization: |
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Algebraic number theory Cellular automata, discrete dynamical systems and complex networks Combinatorial and additive number theory Computational methods for solving algebraic and differential systems Cryptography Geometric curve flows Graph theory and algorithmic game theory Group and ring theory High performance parallel computing Mathematical music theory Mathematical physics and general relativity Mathematics education Nonlinear functional analysis and applications to optimization, game theory, mathematical economics, and differential systems Solitons and integrability of partial differential equations Symmetry analysis and computer algebra applied to nonlinear differential equations |
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Nonparametric statistical inference theory and methods Extreme value theory and applications Quantile regression method and applications Experimental design and regression theory and methods Applied probability, stochastic models and queueing network Survival analysis and risk models Probability distribution theory and applications Monte Carlo simulations and resampling techniques Multivariate analysis Accelerated life testing Linear, generalized linear, and nonlinear models Robust statistics Computational methods and applications to stochastic models Convergence and efficiency of Markov chain Monte Carlo algorithms Optimal design of experiments |
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Successful completion of four year Bachelor's degree, or equivalent, in Mathematics or Statistics, or a related field, with an average of not less than B+ . Agreement from a faculty advisor to supervise the student is also required for admission to the program. The Graduate Admissions Committee will review all applications and recommend admission for a limited number of candidates. Those lacking sufficient background preparation may be required to complete a qualifying term or year to upgrade their applications. Completion of a qualifying term or year does not guarantee acceptance into the program. Part-time study is available. |
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Mathematics Thesis Option:
Major Research Paper Option:
Statistics Thesis Option:
Major Research Paper Option:
Courses by Specialization: Mathematics List A Core courses: MATH 5P10 Groups, Rings, and Group Rings MATH 5P35 Graph Theory MATH 5P50 Algebraic Number Theory MATH 5P66 Matrix Groups and Linear Representations List B Core courses: MATH 5P20 Computational Methods for Algebraic and Differential Systems MATH 5P30 Dynamical Systems MATH 5P40 Functional Analysis MATH 5P60 Partial Differential Equations Non-core courses: MATH 5P09 Solitons and Nonlinear Wave Equations MATH 5P11 Advanced Algebraic Structures MATH 5P31 Ergodicity, Entropy and Chaos MATH 5P36 Algorithmic Game Theory MATH 5P41 Nonlinear Functional Analysis MATH 5P64 Differential Geometry and Mathematical Physics MATH 5P70 Topology MATH 5P75 Theory of Financial Mathematics MATH 5P92 Cryptography MATH 5P96 Technology and Mathematics Education Statistics Core courses: STAT 5P81 Sampling Theory STAT 5P82 Nonparametric Statistics STAT 5P83 Linear Models STAT 5P84 Time Series Analysis and Stochastic Processes STAT 5P85 Mathematical Statistical Inference STAT 5P86 Multivariate Statistics Non-core courses: STAT 5P87 Computational Statistics STAT 5P88 Advanced Statistics STAT 5P89 Bayesian and Causal Bayesian Networks STAT 5P95 MSc Statistics Seminar |
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Each graduate student will be provided with personal desk space and a desktop PC linked to the university network system. In addition, graduate students will have access to the Mathematics computer lab as well as to computer labs located in the vicinity of the Department of Mathematics and Statistics. Software includes a wide array of both commercial and open source applications for supporting research in mathematics and statistics. Brock is also a full member of the SHARCNET consortium with access to all its high performance clusters of powerful workstations and vast storage resources. |
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Note that not all courses are offered in every session. Refer to the applicable timetable for details. Students must ensure that prerequisites are met. Students may be deregistered, at the request of the instructor, from any course for which prerequisites and/or restrictions have not been met. MATHEMATICS COURSES MSc Thesis (also offered as STAT 5F90) Research project involving the preparation of a thesis which will demonstrate a capacity for independent work. The research shall be carried out under the supervision of a faculty member. Solitons and Nonlinear Wave Equations (also offered as PHYS 5P09) Introduction to solitons: Linear and nonlinear travelling waves. Nonlinear evolution equations (Korteweg de Vries, nonlinear Schrodinger, sine-Gordon). Soliton solutions and their interaction properties. Lax pairs, inverse scattering, zero-curvature equations and Backlund transformations, Hamiltonian structures, conservation laws. Note: taught in conjunction with MATH 4P09. Groups, Rings, and Groups Rings Advanced group theory and ring theory, such as group actions, p-groups and Sylow subgroups, solvable and nilpotent groups, finiteness conditions in rings, semisimplicity, the Wedderburn-Artin theorem. Introduction to group rings, such as unit groups, augmentation ideals, several important types of units, and the isomorphism problem. Advanced Algebraic Structures Topics may include: Algebraic coding theory; Combinatorial group theory; Advanced structures in ring theory. Note: Taught in conjunction with either 4P11 or 4P13 Computational Methods for Algebraic and Differential Systems (also offered as PHYS 5P20) Computer algebra applications of solving polynomial systems of algebraic and differential systems of equations are covered, including the necessary algebraic background. Polynomials and ideals, Groebner bases, affine varieties, solving by elimination, Groebner basis conversion, solving equations by resultants, differential algebra, differential Groebner bases. High Performance Computing Parallel computing architectures, new programming models, pilot parallel framework, parallel programming with MPI, thread-based parallelism, and a final project regarding the application of parallel computing to a mathematical problem. Note: Students entering this course are expected to have a good grasp of basic procedural programming in a language such as C or FORTRAN. Dynamical Systems (also offered as PHYS 5P68) Introduction to dynamical systems and their applications in mathematical modeling. Linear flows, local theory of nonlinear flows, linearization theorems, stable manifold theorem. Global theory: limit sets and attractors, Poincare´-Bendixson theorem. Structural stability and bifurcations of vector fields. Low dimensional phenomena in discrete dynamics. Chaotic dynamics: routes to chaos, characterization of chaos and strange attractors. Ergodicity, Entropy and Chaos Introduction to ergodic theory, invariant measures, Birkhoff ergodic theorem. The first return formula, Kac's lemma, recurrence theorems. Entropy, coding maps, Shannon-McMillian Breiman theorem. Chaos, predictability, Lyapunov exponent, speed of divergence, Pesin theorem. Ergodicity, entropy and chaos in shift dynamical systems and cellular automata. Graph Theory Basic definitions, paths and cycles, connectivity, trees and forests, bipartite graphs, Eulerian graphs; Matchings in bipartite graphs and in general graphs; Planar graphs, Euler's formula and Kuratowski's theorem. Graph colourings, Brooks' and Vizing's theorem and colouring of planar graphs; Network flows, Min-Max Theorem. Algorithmic Game Theory Basic definitions, games, strategies, costs and payoffs, equilibria, cooperative games; Complexity of finding Nash equilibria; Mechanism design; Combinatorial auctions; Profit maximization in mechanism design; Cost sharing; Online mechanisms; Inefficiency of equilibria; Selfish routing; Network formation games; Potential function method; The price of anarchy; Sponsored search auctions. Functional Analysis Basic theory of Hilbert spaces, including the Projection Theorem, the Riesz Representation Theorem and the weak topology; weak derivatives, Sobolev spaces and the Sobolev Imbedding Theorem; the variational formulation of boundary value problems for ordinary and partial differential equations, the Lax-Milgram Lemma and its applications; the finite element method. Nonlinear Functional Analysis Topological fixed point theory with applications to dynamical systems and optimization. Topics include the theorems of Brouwer, Borsuk, Schauder-Tychnoff, and Kakutani as well as the Knaster-Kuratowski-Mazurkiewicz principle. Applications of these landmark results to the solvability and qualitative analysis of dynamical systems as well as convex and non-convex optimization are discussed. Algebraic Number Theory Introduction to algebraic aspects of number theory. Topics include the general theory of factorization of ideals in Dedekind domains and number fields, Kummer's theory on lifting of prime ideals in extension fields, factorization of prime ideals in Galois extensions, local fields, the proof of Hensel's lemma, arithmetic of global fields. Partial Differential Equations (also offered as PHYS 5P60) Review of linear and nonlinear equations in two variables. Existence and uniqueness theory, fundamental solutions, initial/boundary-value formulas for the heat equation, wave equation, Laplace equation in multi-dimensions. Exact solution techniques for 1st and 2nd order linear and nonlinear equations. Analysis of solutions, variational formulations, conservation laws, Noether's theorem. Differential Geometry and Mathematical Physics (also offered as PHYS 5P64) Topics may include: Lagrangian and Hamiltonian mechanics, field theory, differential geometric structures, Lie groups and Lie algebras, G-bundles, manifolds, introduction to algebraic topology. Applications to theoretical physics. Matrix groups and linear representations (also offered as PHYS 5P66) Abelian groups, permutation groups, rotation groups. Representations of discrete and continuous groups by linear transformations (matrices). General properties and constructions of group representations. Representations of specific groups. Lie groups and Lie algebras. Applications in various areas of Mathematics, including invariant theory and group algebras, and Theoretical Physics, including crystallography and symmetries in quantum systems. Introduction to Scientific Computing (also offered as PHYS 5P10) Survey of computational methods and techniques commonly used in condensed matter physics research; use of common subroutine libraries; symbolic computing systems; case studies from various areas of computational science; an independent- study term project. Use of graphing and visualization software. Numerical differentiation and integration. Use of special functions. Monte Carlo and molecular dynamics simulation of structure, energetic and thermodynamic properties of metallic, semiconducting and ionic solids and nanoparticles. Topology Introduction to point set topology concepts and principles. Metric spaces; topological spaces; continuity, compactness; connectedness; countability and separation axioms; metrizability; completeness; Baire spaces. Theory of Financial Mathematics Mathematical models arising in modern investment practices. Compound interest, annuities, the time-value of money, Markowitz portfolio theory, efficient frontier, random walks, Brownian processes, future contracts, European and American options, and put-call parity. Introduction to Black-Scholes. Note: Taught in conjunction with MATH 3P75 Cryptography and Number Theory Topics may include RSA cryptosystems, ElGamal cryptosystem, algorithms for discrete logarithmic problem, elliptic curves, computing point multiples on elliptic curves, primality testing and factoring algorithms. Note: taught in conjunction with MATH 4P92. Technology and Mathematics Education Critical appraisal of mathematics learning and teaching with digital technology, such as computer algebra systems and dynamic geometry software. Topics may include contemporary research concerning impact on curriculum; use in assessment; implementation in the classroom; mathematics computer games; use in modeling, probability, and statistics; data science and computational thinking; and the design of a rich technology-based mathematical activity. Note: taught in conjunction with MATH 4P96. Major Research Paper (also offered as STAT 5P99) Survey paper on a topic chosen in consultation with a supervisor from one of the research areas of specialization. Selected Topics in Mathematics and Statistics Investigation of a specific area or group of related topics in mathematics or statistics. STATISTICS COURSES MSc Thesis (also offered as MATH 5F90) Research project involving the preparation of a thesis which will demonstrate a capacity for independent work. The research shall be carried out under the supervision of a faculty member. Sampling Theory Theory of finite population sampling; simple random sampling; sampling proportion; estimation of sample size; Stratified sampling; optimal allocation of sample sizes; ratio estimators; regression estimators; systematic and cluster sampling; multi-stage sampling; error in surveys; computational techniques and computer packages, and related topics. Case studies. Note: taught in conjunction with STAT 4P81 Nonparametric Statistics Order statistics; rank tests and statistics; methods based on the binomial distribution; contingency tables; Kolmogorov-Smirnov statistics; nonparametric analysis of variance; nonparametric regression; comparisons with parametric methods. Computational techniques and use of SAS, Maple or other statistical packages, Case Studies. Note: taught in conjunction with STAT 4P82. Linear Models Classical linear model, generalized inverse matrix, distribution and quadratic forms, regression model, nested classification and classification with interaction, covariance analysis, variance components, binary data, polynomial data, log linear model, linear logit models, generalized linear model, conditional likelihoods, quasi-likelihoods, estimating equations, computational techniques and related topics. Prerequisite(s): STAT 3P86 (or equivalent) or permission of the instructor. Time Series Analysis and Stochastic Processes Multivariate, marginal and conditional Normal distributions; partial correlation coefficient. Probability density function of sample correlation coefficient. Autoregressive and Moving Average models; stability analysis; serial correlation; forecasting; maximum likelihood estimation. Spectral theory; spectrum of ARMA models; aliasing. Spectral analysis of time-series data; periodogram; data filtering; spectrum smooting by spectral or lag window; using a kernel. Estimating serial correlation; large-sample theory. Mathematical Statistical Inference Revision of probability theory, convergence of random variables, statistical models, sufficiency and ancillarity, point estimation, likelihood theory, optimal estimation, Bayesian methods, computational methods, minimum variance estimation, interval estimation and hypothesis testing, linear and generalized linear models, goodness-of-fit for discrete and continuous data, robustness, large sample theory, Bayesian inference. Multivariate Statistics Theory of multivariate statistics, matrix algebra and random vector, sample geometry and random sampling, multivariate normal distribution, inference about means, covariance matrix, generalized Hotelling's T-square distribution, sample covariance and sample generalized variance, Wishart distribution, general hypothesis testing, analysis of variance and linear regression model, principle components, factor analysis, covariance analysis, canonical correlation analysis, discrimination and classification, cluster analysis and related topics. Prerequisite(s): STAT 3P86 (or equivalent) or permission of the instructor. Computational Statistics Classification: logistic regression, linear and quadratic discriminant analysis. Resampling methods: cross-validation and bootstrap. Linear model selection and regularization: subset selection, shrinkage methods, dimension reduction methods, considerations in high dimensions. Nonlinear regression: polynomial regression, regression splines, smoothing splines, local regression and generalized additive models. Tree-based methods: decision trees, bagging, random forests, and boosting. Support vector machines: maximal margin classifier, support vector classifier, support vector machines (SVMs), SVMs with more than two classes. Unsupervised learning: principal component analysis, clustering methods. Prerequisite(s): STAT 3P82 and STAT 3P86 (or their equivalences), or permission of the instructor. Advanced Statistics Topics may vary year to year. Advanced methods and theory in statistical inference, survival analysis, risk analysis, sampling techniques, bootstrapping, Jackknife, generalized linear models, mixed models, modern computational statistics, quality control, life data modeling, biostatistics, multivariate analysis, time series analysis and related topics. Bayesian and Causal Bayesian Networks Representation (exploiting probabilistic independence properties, d-separation, IMap, naive Bayes); parameter learning (maximum likelihood estimation, Bayesian parameter estimation); structure learning (constrained-based approaches, score-based approaches, Bayesian model averaging); learning with incomplete data (parameter and structure learning, learning with hidden variables); causal Bayesian networks (correlation and causation, intervention, inference, learning). Use of R or Python.Lectures, 3 hours per week; lab/tutorial, 1 hour per week Prerequisite(s): STAT 2P81, STAT 3P86, or the permission of the instructor. Statistics Seminar Independent study and presentation of major research papers in areas of specialization. Note: this course will be evaluated as Credit/No-Credit. Major Research Paper (also offered as MATH 5P99) Survey paper on a topic chosen in consultation with a supervisor from one of the research areas of specialization. |
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2023-2024 Graduate Calendar
Last updated: May 24, 2023 @ 01:37PM