
Debra Boutin
Debra Boutin's mathematical interests include graph theory, geometric graph theory and group theory.
The curriculum delves deeply into a wide range of the many and varied branches of mathematics. As a math lover at Hamilton, you’ll find yourself congregating with like-minded peers, talking shop outside your professors’ offices in CJ. You’ll learn within a curriculum that delves deeply into the many branches of math and take courses that foster deductive reasoning, persuasive writing and analytical and quantitative problem-solving.
Students explore both the abstract, theoretical aspects of math and its applications to a variety of topics. Working closely with professors, many students produce work that they go on to present at a professional conference. Mathematics is both a popular major and a crucial part of Hamilton’s broader liberal arts curriculum. Statistics is a minor within the department.
I don’t live and breathe what I majored in. However, there’s no doubt in my mind that my Hamilton education was integral in preparing me to do all of the types of things I do today…Hamilton prepared me in so much as it taught me to think.
Mark Kasdorf — math major
Ancient thinkers recognized that mathematics was the language of the natural world. Today we recognize that it is also the language of science and social science, of business, commerce and industry, even of art and design. Doing math can be as simple as executing a computer search and as momentous as planning a mass evacuation or tracing a disease epidemic, and it assumes ever-greater importance in our lives.
Debra Boutin's mathematical interests include graph theory, geometric graph theory and group theory.
Clark Bowman’s favorite courses to teach have been in computational probability and statistics.
Jose Ceniceros' doctoral research focused on the classification of transverse knots in contact 3-manifolds.
Among Sally Cockburn’s teaching interests are set theory and the philosophical foundations of mathematics.
Coscia’s research interests are in probabilistic combinatorics and graph theory.
Andrew Dykstra's research is in dynamical systems. He is especially interested in symbolic dynamics and ergodic theory.
Courtney Gibbons has developed a research program involving undergraduates in her work in commutative algebra.
Robert Kantrowitz ’82 conducts research in mathematical analysis.
Chinthaka Kuruwita's research is focused on new regression models.
Michelle LeMasurier received an excellence in teaching award.
Tural Sadigov’s interests include probability, statistics, and machine learning.
Richard Bedient's interests are low dimensional topology, knot theory, fractal geometry and chaos theory.
Timothy Kelly received two awards for teaching from Hamilton.
Robert Redfield’s areas of expertise include lattice-ordered fields, rings and groups, vector lattices and ordered topological spaces.
An introduction to linear algebra: matrices and determinants, vector spaces, linear transformations, linear systems and eigenvalues; mathematical and physical applications. Writing-intensive. Quantitative and Symbolic Reasoning.
View All CoursesAn introduction to point set topology, a foundational topic for much of modern mathematics. We will cover topological spaces, separation axioms, quotient spaces, compactness, connectedness, path connectedness, and homotopy. In the last part of the course we will cover the fundamental group, the most basic algebraic topological invariant. Quantitative and Symbolic Reasoning. Oral Presentations.
View All CoursesAn introduction to the theory and applications of graph theory. Topics include: trees; connectivity; Eulerian and Hamiltonian graphs; vertex-, edge- and map-colorings; digraphs; tournaments; matching theory; planarity and Ramsey numbers. Quantitative and Symbolic Reasoning.
View All CoursesTheory and applications of partial differential equations. Topics include separation of variables, Fourier series and transforms, and the Laplace, heat and wave equations. Quantitative and Symbolic Reasoning.
View All CoursesStudy of the mathematical theory underlying classical and modern techniques in statistics, including implementation and visualization in the statistical programming language R. Topics include linear models and regression, randomization and bootstrap, Monte Carlo methods and sampling, Bayesian statistics, and topics in applied statistical modeling. Quantitative and Symbolic Reasoning.
View All CoursesNumber theory is the study of the properties of the positive integers. Topics include divisibility, congruences, quadratic reciprocity, numerical functions, Diophantine equations, continued fractions, distribution of primes. Applications will include cryptography, the practice of encrypting and decrypting messages, and cryptanalysis, the practice of developing secure encryption and decryption protocols and probing them for possible flaws. Quantitative and Symbolic Reasoning.
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