Adam Towsley Headshot

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

Personal Links
Areas of Expertise

Currently Teaching

MATH-182
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
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
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
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.