DCF94280-E8F7-F166-A62F886D097067AC
DD020F53-C98F-50DB-CEDFC9E5288EEEAA

About the Major

Ancient thinkers recognized mathematics as the language of the natural world. Today we know it drives science and social science, business and industry, even art and design. Math majors at Hamilton explore both the abstract, theoretical aspects of mathematics and its applications to a variety of topics. The curriculum delves into the many branches of math, and courses foster deductive reasoning, persuasive writing, and analytical and quantitative problem-solving. Those who love math will find themselves among like-minded peers and mentors, talking shop outside their professors' offices and producing research that they may present at a professional conference. The department also offers a minor in statistics.

Students Will Learn To:

  • Use mathematical and/or statistical tools to model real-world problems
  • Construct mathematical proofs based on rules of logical inference
  • Communicate complete mathematical and/or statistical arguments

A Sampling of Courses

Math Lounge

Graph Theory

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.

Explore these select courses:

An 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.

An introduction to set-theoretic probability with applications to mathematical statistics. Topics include probability spaces, discrete and continuous random variables, single and multivariate distributions, and limit theorems, leading into the mathematical theory of estimators, sampling distributions, confidence intervals, and hypothesis testing.

This course covers statistical methods in machine learning such as decision trees, random forests and support vector machines The course will use a project-based approach to give students hands-on experience using these techniques by analyzing large and complex real-world datasets. More importantly, they will learn the statistical principles behind these procedures, such as loss functions, maximum likelihood estimation and bias-variance trade-off as well as why these principles matter in real world settings.

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. 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. Speaking Intensive.

Our world is built of networks: the internet, social networks, transportation networks, communication networks, biological networks. Natural and useful question include "What makes a network robust?" "Can we predict where failures might occur?" "What can we do to slow propagation of viruses along a network?" This courses will cover abstract mathematical properties of networks that can help us answer these questions. These will be examined in the context of both theoretical and real world networks. Further, student groups will analyze and report on a real world network of their choice.

Meet Our Faculty

Michelle LeMasurier

Chair, Professor of Mathematics

mlemasur@hamilton.edu

dynamical systems and topological dynamics

Saber Ahmed

Assistant Professor of Mathematics and Statistics

smahmed@hamilton.edu

noncommutative algebras, Lie (super)algebras, quantum (super) groups, and representation theory

Clark Bowman

Assistant Professor of Mathematics and Statistics

cbowman@hamilton.edu

uncertainty quantification, probabilistic modeling and simulation, mathematical biology, and high-performance computing

Jose Ceniceros

Associate Professor of Mathematics

jcenicer@hamilton.edu

low dimensional topology; knot theory; Heegaard Floer homology; differential geometry; contact geometry

Sally Cockburn

Samuel F. Pratt Professor of Mathematics & Statistics

scockbur@hamilton.edu

discrete mathematics, particularly graph theory and combinatorial optimization, with a secondary teaching interest in philosophy of mathematics

Andrew Dykstra

Associate Chair, Professor of Mathematics

adykstra@hamilton.edu

dynamical systems, symbolic dynamics, and ergodic theory

Courtney Gibbons

Associate Professor of Mathematics

crgibbon@hamilton.edu

commutative algebra, homological algebra, and applied algebra

Robert Kantrowitz

Marjorie and Robert W. McEwen Professor of Mathematics

rkantrow@hamilton.edu

analysis and commutative Banach algebras

Chinthaka Kuruwita

Associate Professor of Statistics, Director of Data Science

ckuruwit@hamilton.edu

nonparametric density estimation and quantile regression models

Erin Tripp

Assistant Professor of Mathematics

etripp@hamilton.edu

Mathematical optimization, machine learning, signal and image processing

James Burton

Lecturer in Mathematics and Statistics

jjburton@hamilton.edu

Careers After Hamilton

Hamilton graduates who majored in mathematics are pursuing careers in a variety of fields, including:

  • 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
  • Actuarial Analyst, GEICO
  • Math Teacher, Midlakes High School
  • Infectious Disease Epidemiologist, UNC Gillings School of Global Public Health

Explore Hamilton Stories

Grisha Hatavets ’25, left, works with mathematics professor Sally Cockburn in the math lounge in Christian Johnson building.

Optimizing Orientation

While traversing the scenic peaks of the Adirondacks or canoeing through quiet backcountry streams, few first-year students are thinking about algorithms and linear optimization. But these mathematical ideas are as much a part of Hamilton orientation trips as any pack or paddle: they ensure that incoming students have the most worthwhile experience possible.

Alex Kim '25 and assistant prof. of music Charlotte Botha

Machine Learning Music

Alex Kim ’25, a music and math double-major, spent his summer exploring machine learning and music through the help of an Emerson grant. With the guidance of Assistant Professor of Music Charlotte Botha, Kim developed a vocal register classification tool through machine learning models.

2024 new faculty members

Hamilton Welcomes New Faculty for 2024-25

Forty-five new faculty members have entered Hamilton’s ranks, including nine tenure-track professors in anthropology, art, computer science, dance and movement studies, economics, government, mathematics and statistics, music, and Russian. They join 31 visiting professors and lecturers, and four teaching fellows for the 2024-25 academic year.

Contact

Department Name

Mathematics and Statistics Department

Contact Name

Michelle LeMasurier, Chair

Office Location
198 College Hill Road
Clinton, NY 13323

Help us provide an accessible education, offer innovative resources and programs, and foster intellectual exploration.

Site Search