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
An applied and computational mathematics master’s degree that is 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
 Apply mathematical models and methods to study various problems that arise in industry and business, with an emphasis on developing computable solutions.
 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.
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. 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 of RIT’s master's in applied mathematics is to find computing solutions to realworld problems.
Applied and Computational Mathematics
The ideas of applied mathematics pervade several applications in a variety of businesses and industries as well as the government. The reasoning, deduction, and logic skills developed in this program will make you more competitive in a wide array of positions and industries.
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.
RIT’s Master’s in Applied Mathematics
RIT's mathematics master's provides you 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.
Tailor the applied and computational mathematics master’s degree to fit your career goals. Electives may be selected from the graduate course offerings in the School of Mathematical Sciences or from other RIT graduate programs, with approval from the graduate program director. You also 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.
Applied Mathematics Careers
Graduates of the masters in applied mathematics are uniquely qualified to address the full breadth of mathematical challenges and have developed a depth of knowledge in their chosen specializations.
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.
Recent graduates are employed as engineers, economists, analysts (e.g. operations research), physicists, cryptanalysts (codes), actuaries, teachers, market researchers, and financial advisors. Apple, BAE Systems, Ernst & Young, IBM, and Microsoft are just a few of the employers who have hired graduates of the applied and computational mathematics program.
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
What makes an RIT science and math education exceptional? It’s the ability to complete science and math coops and gain realworld experience that sets you apart. Coops in the College of Science include cooperative education and internship experiences in industry and health care settings, as well as research in an academic, industry, or national lab. These are not only possible at RIT, but are passionately encouraged.
What makes an RIT education exceptional? It’s the ability to complete relevant, handson career experience. At the graduate level, and paired with an advanced degree, cooperative education and internships give you the unparalleled credentials that truly set you apart. Learn more about graduate coop and how it provides you with the career experience employers look for in their next top hires.
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
How Mathematicians Move Business Forward
Emily (Kiesel) Wert ’18 (applied & computational mathematics)
Emily (Kiesel) Wert has shown business leaders how valuable mathematicians are on any data analysis and problemsolving team.
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 language requirements. International applicants may be considered for an English test requirement waiver. Refer to the English Language Test Scores section within Graduate Application Materials 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
Students in the master's in applied mathematics program are engaged in research projects covering topics that include:
 Bio modeling
 Brain imaging
 Climate modeling
 Cryptography
 Fluid flow
 Machine learning
 Oil recovery
 Plastic pollution
 Relativity
The School of Mathematical 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

March 21, 2022
RIT Master Plan cuts tuition in half for eligible alumni
RIT is extending a special graduate tuition scholarship program to recent alumni as the COVID19 pandemic enters its third year. The program helps alumni who graduated during the pandemic enhance their skill set for the new economy through master’s degrees that build upon collaboration, analytical thinking, complex problem solving, and flexibility.

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
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