Richard Bedient's research and teaching interests are low dimensional topology, knot theory, fractal geometry and chaos 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.
About the Major
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.
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 ’06 — 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.
Careers After Hamilton
- Financial Analyst, The New York Times
- Resident Physician, Westchester Medical Center
- Business Analyst, Federal Reserve Bank of New York
- Professor of Industrial Engineering, Northeastern University
- Software Engineer, Mitre Corp.
- Legal Analyst, Department of Justice
- Data Miner/Predictive Modeler, Verizon Communications
- Math Teacher, Midlakes High School
Linear Algebra 224FS
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 Courses
Statistical Analysis of Data 253FS
An introduction to the principles and methods of applied statistics. Topics include exploratory data analysis, sampling distributions, confidence intervals, hypothesis testing, regression analysis, analysis of variance and categorical data analysis. Extensive reliance on authentic data and statistical computer software. Quantitative and Symbolic Reasoning.View All Courses
Graph Theory 322S
An 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 Courses
Partial Differential Equations 337S
Theory 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 Courses
Mathematical Statistics and Applications 352S
Study of the mathematical theory underlying statistical methodology. Topics include the law of large numbers, estimation, hypothesis testing, linear models, experimental design, analysis of variance and nonparametric statistics, with applications to a variety of disciplines. Quantitative and Symbolic Reasoning.View All Courses
Number Theory 361S
Number 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. Quantitative and Symbolic Reasoning.View All Courses