Adam Towsley
Senior Lecturer
School of Mathematics and Statistics
College of Science
585-475-6832
Office Hours
M: 2:00--3:00pm (on zoom) Tr: 10:30--11:30pm (in HLC-2223) and by appointment
Office Location
Adam Towsley
Senior Lecturer
School of Mathematics and Statistics
College of Science
Education
MA, Ph.D., University of Rochester
585-475-6832
Areas of Expertise
mathematics
number theory
algebraic number theory
arithmetic dynamics
Currently Teaching
MATH-182
Calculus II
4 Credits
This is the second in a two-course sequence. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers techniques of integration including integration by parts, partial fractions, improper integrals, applications of integration, representing functions by infinite series, convergence and divergence of series, parametric curves, and polar coordinates.
MATH-190
Discrete Mathematics for Computing
3 Credits
This course introduces students to ideas and techniques from discrete mathematics that are widely used in Computer Science. Students will learn about the fundamentals of propositional and predicate calculus, set theory, relations, recursive structures and counting. This course will help increase students’ mathematical sophistication and their ability to handle abstract problems.
MATH-241
Linear Algebra
3 Credits
This course is an introduction to the basic concepts of linear algebra, and techniques of matrix manipulation. Topics include linear transformations, Gaussian elimination, matrix arithmetic, determinants, vector spaces, linear independence, basis, null space, row space, and column space of a matrix, eigenvalues, eigenvectors, change of basis, similarity and diagonalization. Various applications are studied throughout the course.
MATH-305
Introduction to Mathematical Computing
3 Credits
This course is an introduction to the use and application of scientific computing packages to explore methodologies (graphical, numerical, and symbolic) to study problems arising in undergraduate courses in science, engineering and mathematics. Specific topics include numerical differentiation and integration, optimization, initial value problems, linear systems of equations, and applications in data science.