Applied and Computational Mathematics Master of science degree
Applied and Computational Mathematics
Master of science degree
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 Applied and Computational Mathematics MS
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School of Mathematical Sciences
Overview
A mathematics master's degree designed for you to create innovative computing solutions, mathematical models, and dynamic systems to solve problems in industries such as engineering, biology, and more.
Sophisticated mathematical tools are increasingly used to solve problems in management science, engineering, biology, financial portfolio planning, facilities planning, control of dynamic systems, and design of composite materials. The goal is to find computing solutions to realworld problems. The applied and computational mathematics master’s degree refines your capabilities in applying mathematical models and methods to study a range of problems, with an emphasis on developing and implementing computing solutions.
The ideas of applied mathematics pervade several applications in a variety of businesses and industries as well as the government. Sophisticated mathematical tools are increasingly used to develop new models, modify existing ones, and analyze system performance. This includes applications of mathematics to problems in management science, biology, portfolio planning, facilities planning, control of dynamic systems, and design of composite materials. The goal of this mathematics master's degree is to find computable solutions to realworld problems arising from these types of situations.
The masters of science degree in applied and computational mathematics provide students with the capability to apply mathematical models and methods to study various problems that arise in industry and business, with an emphasis on developing computable solutions that can be implemented. The program offers concentrations in discrete mathematics, dynamical systems, and scientific computing. Electives may be selected from the graduate course offerings in the School of Mathematical Sciences or from other graduate programs, with approval from the graduate program director. Students have the option to complete a thesis, which includes the presentation of original ideas and solutions to a specific mathematical problem. The proposal for the thesis work and the results must be presented and defended before the advisory committee.
Nature of work
Mathematicians use mathematical theory, computational techniques, algorithms, and the latest computer technology to solve economic, scientific, engineering, physics, and business problems. The work of mathematicians falls into two broad classes — theoretical (pure) mathematics and applied mathematics. These classes, however, often overlap. Applied mathematicians start with a practical problem, envision its separate elements, and then reduce the elements to mathematical variables. They often use computers to analyze relationships among the variables, and they solve complex problems by developing models with alternative solutions.
Types of mathematics
Most often the work involving applied mathematics is done by persons whose titles are other than mathematician, including engineer, economist, analyst (e.g. operations research), physicist, cryptanalyst (codes), actuary, teacher, market researcher, and financial advisor.
Many mathematicians work for federal or state agencies. The Department of Defense accounts for about 81 percent of the mathematicians employed by the federal government. In the private sector, mathematicians are employed by scientific research and development services, software publishers, insurance companies, and in aerospace or pharmaceutical manufacturing.
Parttime study
The program is ideal for practicing professionals who are interested in applying mathematical methods in their work and enhancing their career options. Most courses are scheduled in the late afternoon or early evening. The program may normally be completed in two years of parttime study.
Industries

Biotech and Life Sciences 
Defense 
Government (Local, State, Federal) 
Insurance 
Investment Banking 
Telecommunications
Typical Job Titles
Engineer  Economist 
Analyst (e.g. Operations Research)  Physicist 
Cryptanalyst (codes)  Actuary 
Teacher  Market Researcher 
Financial Advisor 
Cooperative Education
Cooperative education, or coop for short, is fulltime, paid work experience in your field of study. And it sets RIT graduates apart from their competitors. It’s exposure–early and often–to a variety of professional work environments, career paths, and industries. RIT coop is designed for your success.
Coop is optional for students in the applied and computational mathematics program. Students may pursue a coop position after their first semester.
Explore salary and career information for Applied and Computational Mathematics MS
Featured Profiles
Your Partners in Success: Meet Our Faculty
Dr. Tony Wong
Mathematics is a powerful tool for answering questions. From mitigating climate risks to splitting the dinner bill, Professor Wong shows students that math is more than just a prerequisite.
Curriculum for Applied and Computational Mathematics MS
Applied and Computational Mathematics (thesis option), MS degree, typical course sequence
Course  Sem. Cr. Hrs.  

First Year  
Choose four of the following core courses:  12  
MATH601 
Methods of Applied Mathematics
This course is an introduction to classical techniques used in applied mathematics. Models arising in physics and engineering are introduced. Topics include dimensional analysis, scaling techniques, regular and singular perturbation theory, and calculus of variations. (Prerequisites: MATH221 and MATH231 or equivalent courses or students in the ACMTHMS or MATHMLPHD programs.) Lecture 3 (Spring).


MATH602 
Numerical Analysis I
This course covers numerical techniques for the solution of nonlinear equations, interpolation, differentiation, integration, and matrix algebra. (Prerequisites: (MATH241 and MATH431) or equivalent courses or graduate standing in ACMTHMS or MATHMLPHD programs.) Lecture 3 (Fall).


MATH605 
Stochastic Processes
This course is an introduction to stochastic processes and their various applications. It covers the development of basic properties and applications of Poisson processes and Markov chains in discrete and continuous time. Extensive use is made of conditional probability and conditional expectation. Further topics such as renewal processes, reliability and Brownian motion may be discussed as time allows. (Prerequisites: (MATH241 and MATH251) or equivalent courses or graduate standing in ACMTHMS or MATHMLPHD or APPSTATMS programs.) Lecture 3 (Spring).


MATH622 
Mathematical Modeling I
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, closedform 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, graphtheoretic 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 is restricted to students in the ACMTHMS or MATHMLPHD programs.) Lecture 3 (Fall).


MATH645 
Graph Theory
This course introduces the fundamental concepts of graph theory. Topics to be studied include graph isomorphism, trees, network flows, connectivity in graphs, matchings, graph colorings, and planar graphs. Applications such as traffic routing and scheduling problems will be considered. (This course is restricted to students with graduate standing in the College of Science or Graduate Computing and Information Sciences.) Lecture 3 (Fall).


MATH722 
Mathematical Modeling II
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. (Prerequisite: MATH622 or equivalent course.) Lecture 3 (Spring).


MATH606 
Graduate Seminar I
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 restricted to students in the ACMTHMS or MATHMLPHD programs.) Lecture 2 (Fall).

1 
MATH607 
Graduate Seminar II
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. (Prerequisite: MATH606 or equivalent course or students in the ACMTHMS or MATHMLPHD programs.) Lecture 2 (Spring).

1 
Electives 
6  
Second Year  
MATH790 
Research & Thesis
Masterslevel research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (This course is restricted to students in the ACMTHMS or MATHMLPHD programs.) Thesis (Fall, Spring, Summer).

7 
Elective 
3  
Total Semester Credit Hours  30 
Applied and Computational Mathematics (project option), MS degree, typical course sequence
Course  Sem. Cr. Hrs.  

First Year  
Choose four of the following core courses:  12 

MATH601 
Methods of Applied Mathematics
This course is an introduction to classical techniques used in applied mathematics. Models arising in physics and engineering are introduced. Topics include dimensional analysis, scaling techniques, regular and singular perturbation theory, and calculus of variations. (Prerequisites: MATH221 and MATH231 or equivalent courses or students in the ACMTHMS or MATHMLPHD programs.) Lecture 3 (Spring).


MATH602 
Numerical Analysis I
This course covers numerical techniques for the solution of nonlinear equations, interpolation, differentiation, integration, and matrix algebra. (Prerequisites: (MATH241 and MATH431) or equivalent courses or graduate standing in ACMTHMS or MATHMLPHD programs.) Lecture 3 (Fall).


MATH605 
Stochastic Processes
This course is an introduction to stochastic processes and their various applications. It covers the development of basic properties and applications of Poisson processes and Markov chains in discrete and continuous time. Extensive use is made of conditional probability and conditional expectation. Further topics such as renewal processes, reliability and Brownian motion may be discussed as time allows. (Prerequisites: (MATH241 and MATH251) or equivalent courses or graduate standing in ACMTHMS or MATHMLPHD or APPSTATMS programs.) Lecture 3 (Spring).


MATH622 
Mathematical Modeling I
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, closedform 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, graphtheoretic 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 is restricted to students in the ACMTHMS or MATHMLPHD programs.) Lecture 3 (Fall).


MATH645 
Graph Theory
This course introduces the fundamental concepts of graph theory. Topics to be studied include graph isomorphism, trees, network flows, connectivity in graphs, matchings, graph colorings, and planar graphs. Applications such as traffic routing and scheduling problems will be considered. (This course is restricted to students with graduate standing in the College of Science or Graduate Computing and Information Sciences.) Lecture 3 (Fall).


MATH722 
Mathematical Modeling II
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. (Prerequisite: MATH622 or equivalent course.) Lecture 3 (Spring).


MATH606 
Graduate Seminar I
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 restricted to students in the ACMTHMS or MATHMLPHD programs.) Lecture 2 (Fall).

1 
MATH607 
Graduate Seminar II
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. (Prerequisite: MATH606 or equivalent course or students in the ACMTHMS or MATHMLPHD programs.) Lecture 2 (Spring).

1 
Electives 
6  
Second Year  
MATH790 
Research & Thesis
Masterslevel research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (This course is restricted to students in the ACMTHMS or MATHMLPHD programs.) Thesis (Fall, Spring, Summer).

4 
Electives 
6  
Total Semester Credit Hours  30 
Admission Requirements
To be considered for admission to the MS program in applied and computational mathematics, candidates must fulfill the following requirements:
 Complete a graduate application.
 Hold a baccalaureate degree (or equivalent) from an accredited university or college in mathematics or a related field.
 Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
 Have knowledge of a programming language.
 Have a minimum cumulative GPA of 3.0 (or equivalent).
 Submit a personal statement of educational objectives.
 Submit two letters of recommendation from academic or professional sources.
 International applicants whose native language is not English must submit scores from the TOEFL, IELTS, or PTE. A minimum TOEFL score of 79 (internetbased) is required. A minimum IELTS score of 6.5 is required. The English language test score requirement is waived for native speakers of English or for those submitting transcripts from degrees earned at American institutions.
Although Graduate Record Examination (GRE) scores are not required, submitting them may enhance a candidate's acceptance into the program.
A student may also be granted conditional admission and be required to complete bridge courses selected from among RIT’s existing undergraduate courses, as prescribed by the student’s adviser. Until these requirements are met, the candidate is considered a nonmatriculated student. The graduate program director evaluates the student’s qualifications to determine eligibility for conditional and provisional admission.
Nonmatriculated students
A student with a bachelor’s degree from an approved undergraduate institution, and with the background necessary for specific courses, may take graduate courses as a nonmatriculated student with the permission of the graduate program director and the course instructor. Courses taken for credit may be applied toward the master’s degree if the student is formally admitted to the program at a later date. However, the number of credit hours that may be transferred into the program from courses taken at RIT is limited for nonmatriculated students.
Learn about admissions, cost, and financial aid
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