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
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.
Overview
 This program provides 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.
 Graduates work in areas such as mathematical modeling and analysis of manufacturing, computer and communications systems, transportation optimization, financial mathematics, biological modeling, and consulting/planning.
 Current program research includes network science, image analysis, contact lens, polymeric flows, coating, lake plastic, climate modeling, relativity, multimessenger astrophysics, data analytics, and machine learning.
 Apple, BAE Systems, Ernst & Young, IBM, and Microsoft are just a sampling of employers who hire graduates from the applied and computational mathematics program.
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 master's of science degree in applied and computational mathematics provides 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. 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 a 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.
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Careers and Experiential Learning
Typical Job Titles
Network Consulting Engineer  Junior Accountant 
Data Analyst  Software Engineer 
Junior Business Analyst  Data Scientist 
Technical Advisory  Fund Accountant 
Sr. Project Manager 
Salary and Career Information for Applied and Computational Mathematics MS
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.
National Labs Career Fair
Hosted by RIT’s Office of Career Services and Cooperative Education, the National Labs Career Fair is an annual event that brings representatives to campus from the United States’ federally funded research and development labs. These national labs focus on scientific discovery, clean energy development, national security, technology advancements, and more. Students are invited to attend the career fair to network with lab professionals, learn about opportunities, and interview for coops, internships, research positions, and fulltime employment.
Featured Profiles
RIT Alum Uses Math for NBA Analytics
Calvin Floyd ’17 (applied & computational mathematics)
Calvin Floyd developed the strong technical skills needed to succeed in an NBA analytics department during his masters degree program in applied and computational mathematics at RIT.
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 three of the following core courses:  9  
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 or MATH241H) 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 or MATH241H) 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 
MATH Graduate Electives 
9  
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 
MATH Graduate 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 three of the following core courses:  9 

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 or MATH241H) 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 or MATH241H) 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 
MATH Graduate Electives 
9  
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 
MATH Graduate 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 an online graduate application. Refer to Graduate Admission Deadlines and Requirements for information on application deadlines, entry terms, and more.
 Submit copies of official transcript(s) (in English) of all previously completed undergraduate and graduate course work, including any transfer credit earned.
 Hold a baccalaureate degree (or US equivalent) from an accredited university or college in mathematics or a related field.
 Recommended minimum cumulative GPA of 3.0 (or equivalent).
 Submit a current resume or curriculum vitae.
 Two letters of recommendation are required. Refer to Application Instructions and Requirements for additional information.
 Not all programs require the submission of scores from entrance exams (GMAT or GRE). Please refer to the Graduate Admission Deadlines and Requirements page for more information.
 Submit a personal statement of educational objectives. Refer to Application Instructions and Requirements for additional information.
 Have college level credit or practical experience in programming language.
 International applicants whose native language is not English must submit official test scores from the TOEFL, IELTS, or PTE. Students below the minimum requirement may be considered for conditional admission. Refer to Graduate Admission Deadlines and Requirements for additional information on English requirements. International applicants may be considered for an English test requirement waiver. Refer to Additional Requirements for International Applicants to review waiver eligibility.
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 advisor. 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
Research
The College of Science consistently receives research grant awards from organizations that include the National Science Foundation, National Institutes of Health, and NASA, which provide you with unique opportunities to conduct cuttingedge research with our faculty members.
Faculty in the School of Mathematical Sciences conducts research on a broad variety of topics including:
 applied inverse problems and optimization
 applied statistics and data analytics
 biomedical mathematics
 discrete mathematics
 dynamical systems and fluid dynamics
 geometry, relativity, and gravitation
 mathematics of earth and environment systems
 multimessenger and multiwavelength astrophysics
Learn more by exploring the school's mathematics research areas.
Latest News

July 8, 2021
First mathematical modeling Ph.D. student graduates from RIT
From her early days in school, Nicole Rosato realized that math was one of her favorite subjects. This past May, Rosato, who is from Paramus, N.J., became the first student to graduate from RIT’s new Ph.D. program in mathematical modeling.

June 23, 2021
New math model traces the link between atmospheric CO2 and temperature over half a billion years
RIT mathematician Tony Wong helped develop a new modeling method to explore the relationship between the Earth’s atmospheric carbon dioxide (CO2) and surface temperature over hundreds of millions of years.

March 24, 2021
Integrating diverse satellite images sharpens our picture of activity on Earth
Essay by Amanda Ziemann ’10, ’11 MS (applied mathematics), a remote sensing scientist at Los Alamos National Laboratory, published by Space.com.