This course serves two purposes. One is to introduce students to metacognition, reflective practice and self-assessment. Students will explore how the continual assessment of one's own knowledge guides scientific progress in the development of both research and theoretical practice. The second is to apply metacognitive techniques to exploring scientific investigation from a combination of scientific, ethical and societal standpoints. Examples will be drawn from student interest, and may include topics such as: Chernobyl and Fukushima nuclear disasters, genetically modified organisms, indoor air quality, invasive species, forensic science. Metacognitive issues such as learning theory, stereotype threat and self-assessment will be explored for their role in the acquisition of scientific knowledge.

This is the first in a two-course sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers functions, limits, continuity, the derivative, rules of differentiation, applications of the derivative, Riemann sums, definite integrals, and indefinite integrals.

This course is a faculty-guided investigation into appropriate topics that are not part of the curriculum.

This capstone experience introduces students to mathematical problems and situations not encountered in previous courses of study. The class will primarily revolve around student-directed,collaborative efforts to solve a given problem using rigorous mathematical analysis and (as appropriate) computational methods. Significant work outside the classroom will be required of students. Students will write a formal report of their solution methods, and produce a poster for presentation at an end-of-term conference-style event.

The course prepares students to engage in activities necessary for independent mathematical research and introduces students to a broad range of active interdisciplinary programs related to applied mathematics.

This course is a continuation of Graduate Seminar I. It prepares students to engage in activities necessary for independent mathematical research and introduces them to a broad range of active interdisciplinary programs related to applied mathematics.

This course will introduce graduate students to the logical methodology of mathematical modeling. They will learn how to use an application field problem as a standard for defining equations that can be used to solve that problem, how to establish a nested hierarchy of models for an application field problem in order to clarify the problem’s context and facilitate its solution. Students will also learn how mathematical theory, closed-form solutions for special cases, and computational methods should be integrated into the modeling process in order to provide insight into application fields and solutions to particular problems. Students will study principles of model verification and validation, parameter identification and parameter sensitivity and their roles in mathematical modeling. In addition, students will be introduced to particular mathematical models of various types: stochastic models, PDE models, dynamical system models, graph-theoretic models, algebraic models, and perhaps other types of models. They will use these models to exemplify the broad principles and methods that they will learn in this course, and they will use these models to build up a stock of models that they can call upon as examples of good modeling practice.

This course will continue to expose students to the logical methodology of mathematical modeling. It will also provide them with numerous examples of mathematical models from various fields.

Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

Continuation of Thesis

Independent Study