Post-Bachelor's Certificate (CAS) in Computational Mathematical Modeling brochure

SUNY Polytechnic Institute

Quad-C will offer internship and employment opportunities for current and future SUNY Poly students!

SUNY Poly offers a range of popular academic programs, many in fields with increasing demand taught by caring faculty. Advanced and

Certificate for Advanced Study (Post-Bachelor’s)

growing corporate partnerships on the Utica and Albany sites and

Computational Mathematical Modeling

across New York State provide the groundwork for internships and job opportunities. Small classes, quality programs, successful graduates, and an affordable investment are all reasons why we’re an increasingly popular choice among college-bound students.

For More Information: Graduate Admissions Office State University of New York Polytechnic Institute 100 Seymour Road Utica, NY 13502 315-792-7347 or toll free 1 (866) 278-6948 graduate@sunypoly.edu www.sunypoly.edu 8/16

Career Opportunities

The Faculty

The certificate is valuable to applied mathematicians, engineers and computational physicists seeking solutions to problems in diverse areas including material science, medical applications, energy engineering and astronomy.

SUNY Poly faculty work closely with students and challenge them to excel. Many have experience in industry in addition to strong academic credentials. A low student-tofaculty ration (18:1) means faculty really get to know and work closely with students.

Examples of real-world applications: • Material science engineers produce and process new high tech materials using computational mathematical modeling to analyze and predict complex material behavior with micro and nano structures.

Certificate for Advanced Study in Computational Mathematical Modeling

The Program The post-bachelor’s Certificate for Advanced Study in Computational Mathematical Modeling provides education and training for careers in science and industry as well as for further graduate study in the area of physically based computational mathematical modeling and data analysis. To be successful in these areas practitioners must have a sound background in mathematical modeling and good computational skills. The certificate stresses fundamentals and applications equally.

Students will learn how to: • Model, analyze and solve problems from science and engineering using advanced methods from linear algebra, differential equations, and computational mathematics.

• Implement numerical methods for the solution of standard partial and ordinary differential equations, to analyze their mathematical behavior (consistency and stability), and to choose the appropriate method for different types of problems.

• Apply their knowledge of computational mathematical modeling in their area of interest, for example, fluids, elasticity, electromagnetism, quantum mechanics, material science, probability, stochastic modeling, data-analysis, nano-science or biomedical applications.

The program is designed for students with a B.S. in mathematics, physics, engineering, or a comparable program. In addition, a background in mathematics including linear algebra, differential equations, and multivariate calculus is required.

• Science emerged as the interplay between theory and experiment, but increasingly computational modeling helps by simulating experiments, which cannot be done or are too expensive or not safe. For example, computational mathematical modeling and simulation studies are increasingly accepted by the food and drug administration for device development and to decide if a medical device is safe.

• Advances in sensor technology generate large amounts of data, which can be utilized to help improve mathematical models. This approach calls for advanced computational and statistical methods.

Degree Requirements The 12 credit hour Advanced Certificate in Computational Mathematical Modeling consists of 4 courses: MAT 502 Linear Algebra (3 credits) MAT 515 Mathematical Modeling in Computational

Sciences and Engineering (3 credits) MAT 560 Numerical Differential Equations (3 credits) Choose one from the following: MAT 550 Time Series Analysis (3 credits) MAT 505 Introduction to Probability (3 credits) MAT 590 Selected Topics in Mathematics (3 credits) Students should consult with a faculty member to develop an academic plan.

Admission Guidelines To be considered for admission, all applicants to the CAS Computational Mathematical Modeling program must possess a baccalaureate degree from an accredited university or college with an average of B or better (a GPA of 3.0 on a 4.0 scale).

In addition, a background in mathematics including linear algebra, differential equations and multivariable calculus (calculus III) is required.

Applicants not meeting the above admission criteria will be considered on an individual basis.

The Application for Graduate Admission and all required forms are available at: www.sunypoly.edu/graduateadmissions

Carlo Cafaro is a Lecturer in Applied Mathematics with a Ph.D. in Theoretical Physics from SUNY at Albany and a M.S. in Theoretical Physics from University of Pisa in Italy. Dr. Cafaro’s research focuses on the basic foundations of theoretical physics: information theory, quantum theory, and relativity.

More specifically, Cafaro’s recent research efforts involve: quantifying complexity with information geometry and statistical inference; applying geometric Clifford algebra techniques to classical electrodynamics and quantum computing; combatting quantum decoherence with quantum error correction schemes; employing statistical physics methods in complex network science.

Wenfeng Chen is a Lecturer in Applied Mathematics with a Ph.D. in Theoretical High Energy Physics and Mathematical Physics from the Chinese Academy of Science at Beijing. Dr. Chen has done research projects in Quantum Field Theory and Superstring/M-theory including topological ChernSimons theory, nonperturbative supersymmetric gauge Theory, Lorentz and CPT symmetry violation, AdS/CFT correspondence and gauge/gravity duality. Currently, Dr. Chen is focusing on applying the miscellaneous mathematical techniques of quantum field theory and string theory to study condensed matter physics, specifically nanophysics, and topological quantum computation.

Andrea Dziubek is an Assistant Professor of Applied Mathematics with a Ph.D. in Energy and Process Engineering from Berlin University of Technology, Germany. Her research interests include modeling and simulation of problems in biomedical engineering, continuum mechanics, shell theory, structure preserving numerical methods and finite element methods. She loves teaching subjects where she can share her academic interests and excitement, such as vector calculus and geometric mechanics, numerical mathematics, and partial differential equations.

Edmond Rusjan is an Associate Professor of Applied Mathematics with a Ph.D. in Mathematical Physics from Virginia Tech. His research focus is on geometry and symmetry inspired mathematical models. In particular, he has applied the Boltzmann equation, Lie groups and Lie algebras and CalabiYau spaces to solve problems in physics and engineering. He is currently studying the discretization of the Hodge star operator and implications for partial differential equations.

Tural Sadigov is a Lecturer of Applied Mathematics with a Ph.D. and M.S. in Applied Mathematics from Indiana University in Bloomington, Indiana. His research interests include Dynamical Systems and Partial Differential Equations focusing on determining parameters and determining forms for dissipative and semi-dissipative systems. He has been teaching various levels of Calculus, Probability, and Differential Equations. Currently, he is working on determining forms for subcritical Surface QuasiGeostrophic (SQG) Equations, and time series analysis with the main focus on ARIMA models.

William Thistleton is an Associate Professor of Applied Mathematics, with degrees in Electrical Engineering and Mathematics, and a Ph.D. in Applied Mathematics from SUNY Stony Brook. In addition to his extensive teaching experience in areas including Analysis, Computational Mathematics, and Data Analysis, he has scholarly publications in Probability and Statistics. He consults regularly with industry in a variety of settings.

Zora Thomova is a Professor of Applied Mathematics with Ph.D. in Mathematics from University of Montreal, Canada and M.S. in Engineering Physics from the Czech Technical University in Prague.

She is a recipient of the SUNY Chancellor’s Award for Excellence in teaching; her teaching experience includes mathematics courses at both the undergraduate and graduate level to mathematics and engineering students. She also teaches financial mathematics and fundamentals of derivative markets to MBA students and finance professionals at a major investment institution. She regularly publishes in the area of continuous symmetries of differential and difference equations and has served as an adviser on quantitative projects for financial clients.

Career Opportunities

The Faculty

The certificate is valuable to applied mathematicians, engineers and computational physicists seeking solutions to problems in diverse areas including material science, medical applications, energy engineering and astronomy.

SUNY Poly faculty work closely with students and challenge them to excel. Many have experience in industry in addition to strong academic credentials. A low student-tofaculty ration (18:1) means faculty really get to know and work closely with students.

Examples of real-world applications: • Material science engineers produce and process new high tech materials using computational mathematical modeling to analyze and predict complex material behavior with micro and nano structures.

Certificate for Advanced Study in Computational Mathematical Modeling

The Program The post-bachelor’s Certificate for Advanced Study in Computational Mathematical Modeling provides education and training for careers in science and industry as well as for further graduate study in the area of physically based computational mathematical modeling and data analysis. To be successful in these areas practitioners must have a sound background in mathematical modeling and good computational skills. The certificate stresses fundamentals and applications equally.

Students will learn how to: • Model, analyze and solve problems from science and engineering using advanced methods from linear algebra, differential equations, and computational mathematics.

• Implement numerical methods for the solution of standard partial and ordinary differential equations, to analyze their mathematical behavior (consistency and stability), and to choose the appropriate method for different types of problems.

• Apply their knowledge of computational mathematical modeling in their area of interest, for example, fluids, elasticity, electromagnetism, quantum mechanics, material science, probability, stochastic modeling, data-analysis, nano-science or biomedical applications.

The program is designed for students with a B.S. in mathematics, physics, engineering, or a comparable program. In addition, a background in mathematics including linear algebra, differential equations, and multivariate calculus is required.

• Science emerged as the interplay between theory and experiment, but increasingly computational modeling helps by simulating experiments, which cannot be done or are too expensive or not safe. For example, computational mathematical modeling and simulation studies are increasingly accepted by the food and drug administration for device development and to decide if a medical device is safe.

• Advances in sensor technology generate large amounts of data, which can be utilized to help improve mathematical models. This approach calls for advanced computational and statistical methods.

Degree Requirements The 12 credit hour Advanced Certificate in Computational Mathematical Modeling consists of 4 courses: MAT 502 Linear Algebra (3 credits) MAT 515 Mathematical Modeling in Computational

Sciences and Engineering (3 credits) MAT 560 Numerical Differential Equations (3 credits) Choose one from the following: MAT 550 Time Series Analysis (3 credits) MAT 505 Introduction to Probability (3 credits) MAT 590 Selected Topics in Mathematics (3 credits) Students should consult with a faculty member to develop an academic plan.

Admission Guidelines To be considered for admission, all applicants to the CAS Computational Mathematical Modeling program must possess a baccalaureate degree from an accredited university or college with an average of B or better (a GPA of 3.0 on a 4.0 scale).

In addition, a background in mathematics including linear algebra, differential equations and multivariable calculus (calculus III) is required.

Applicants not meeting the above admission criteria will be considered on an individual basis.

The Application for Graduate Admission and all required forms are available at: www.sunypoly.edu/graduateadmissions

Carlo Cafaro is a Lecturer in Applied Mathematics with a Ph.D. in Theoretical Physics from SUNY at Albany and a M.S. in Theoretical Physics from University of Pisa in Italy. Dr. Cafaro’s research focuses on the basic foundations of theoretical physics: information theory, quantum theory, and relativity.

More specifically, Cafaro’s recent research efforts involve: quantifying complexity with information geometry and statistical inference; applying geometric Clifford algebra techniques to classical electrodynamics and quantum computing; combatting quantum decoherence with quantum error correction schemes; employing statistical physics methods in complex network science.

Wenfeng Chen is a Lecturer in Applied Mathematics with a Ph.D. in Theoretical High Energy Physics and Mathematical Physics from the Chinese Academy of Science at Beijing. Dr. Chen has done research projects in Quantum Field Theory and Superstring/M-theory including topological ChernSimons theory, nonperturbative supersymmetric gauge Theory, Lorentz and CPT symmetry violation, AdS/CFT correspondence and gauge/gravity duality. Currently, Dr. Chen is focusing on applying the miscellaneous mathematical techniques of quantum field theory and string theory to study condensed matter physics, specifically nanophysics, and topological quantum computation.

Andrea Dziubek is an Assistant Professor of Applied Mathematics with a Ph.D. in Energy and Process Engineering from Berlin University of Technology, Germany. Her research interests include modeling and simulation of problems in biomedical engineering, continuum mechanics, shell theory, structure preserving numerical methods and finite element methods. She loves teaching subjects where she can share her academic interests and excitement, such as vector calculus and geometric mechanics, numerical mathematics, and partial differential equations.

Edmond Rusjan is an Associate Professor of Applied Mathematics with a Ph.D. in Mathematical Physics from Virginia Tech. His research focus is on geometry and symmetry inspired mathematical models. In particular, he has applied the Boltzmann equation, Lie groups and Lie algebras and CalabiYau spaces to solve problems in physics and engineering. He is currently studying the discretization of the Hodge star operator and implications for partial differential equations.

Tural Sadigov is a Lecturer of Applied Mathematics with a Ph.D. and M.S. in Applied Mathematics from Indiana University in Bloomington, Indiana. His research interests include Dynamical Systems and Partial Differential Equations focusing on determining parameters and determining forms for dissipative and semi-dissipative systems. He has been teaching various levels of Calculus, Probability, and Differential Equations. Currently, he is working on determining forms for subcritical Surface QuasiGeostrophic (SQG) Equations, and time series analysis with the main focus on ARIMA models.

William Thistleton is an Associate Professor of Applied Mathematics, with degrees in Electrical Engineering and Mathematics, and a Ph.D. in Applied Mathematics from SUNY Stony Brook. In addition to his extensive teaching experience in areas including Analysis, Computational Mathematics, and Data Analysis, he has scholarly publications in Probability and Statistics. He consults regularly with industry in a variety of settings.

Zora Thomova is a Professor of Applied Mathematics with Ph.D. in Mathematics from University of Montreal, Canada and M.S. in Engineering Physics from the Czech Technical University in Prague.

She is a recipient of the SUNY Chancellor’s Award for Excellence in teaching; her teaching experience includes mathematics courses at both the undergraduate and graduate level to mathematics and engineering students. She also teaches financial mathematics and fundamentals of derivative markets to MBA students and finance professionals at a major investment institution. She regularly publishes in the area of continuous symmetries of differential and difference equations and has served as an adviser on quantitative projects for financial clients.

Career Opportunities

The Faculty

The certificate is valuable to applied mathematicians, engineers and computational physicists seeking solutions to problems in diverse areas including material science, medical applications, energy engineering and astronomy.

SUNY Poly faculty work closely with students and challenge them to excel. Many have experience in industry in addition to strong academic credentials. A low student-tofaculty ration (18:1) means faculty really get to know and work closely with students.

Examples of real-world applications: • Material science engineers produce and process new high tech materials using computational mathematical modeling to analyze and predict complex material behavior with micro and nano structures.

Certificate for Advanced Study in Computational Mathematical Modeling

The Program The post-bachelor’s Certificate for Advanced Study in Computational Mathematical Modeling provides education and training for careers in science and industry as well as for further graduate study in the area of physically based computational mathematical modeling and data analysis. To be successful in these areas practitioners must have a sound background in mathematical modeling and good computational skills. The certificate stresses fundamentals and applications equally.

Students will learn how to: • Model, analyze and solve problems from science and engineering using advanced methods from linear algebra, differential equations, and computational mathematics.

• Implement numerical methods for the solution of standard partial and ordinary differential equations, to analyze their mathematical behavior (consistency and stability), and to choose the appropriate method for different types of problems.

• Apply their knowledge of computational mathematical modeling in their area of interest, for example, fluids, elasticity, electromagnetism, quantum mechanics, material science, probability, stochastic modeling, data-analysis, nano-science or biomedical applications.

The program is designed for students with a B.S. in mathematics, physics, engineering, or a comparable program. In addition, a background in mathematics including linear algebra, differential equations, and multivariate calculus is required.

• Science emerged as the interplay between theory and experiment, but increasingly computational modeling helps by simulating experiments, which cannot be done or are too expensive or not safe. For example, computational mathematical modeling and simulation studies are increasingly accepted by the food and drug administration for device development and to decide if a medical device is safe.

• Advances in sensor technology generate large amounts of data, which can be utilized to help improve mathematical models. This approach calls for advanced computational and statistical methods.

Degree Requirements The 12 credit hour Advanced Certificate in Computational Mathematical Modeling consists of 4 courses: MAT 502 Linear Algebra (3 credits) MAT 515 Mathematical Modeling in Computational

Sciences and Engineering (3 credits) MAT 560 Numerical Differential Equations (3 credits) Choose one from the following: MAT 550 Time Series Analysis (3 credits) MAT 505 Introduction to Probability (3 credits) MAT 590 Selected Topics in Mathematics (3 credits) Students should consult with a faculty member to develop an academic plan.

Admission Guidelines To be considered for admission, all applicants to the CAS Computational Mathematical Modeling program must possess a baccalaureate degree from an accredited university or college with an average of B or better (a GPA of 3.0 on a 4.0 scale).

In addition, a background in mathematics including linear algebra, differential equations and multivariable calculus (calculus III) is required.

Applicants not meeting the above admission criteria will be considered on an individual basis.

The Application for Graduate Admission and all required forms are available at: www.sunypoly.edu/graduateadmissions

Carlo Cafaro is a Lecturer in Applied Mathematics with a Ph.D. in Theoretical Physics from SUNY at Albany and a M.S. in Theoretical Physics from University of Pisa in Italy. Dr. Cafaro’s research focuses on the basic foundations of theoretical physics: information theory, quantum theory, and relativity.

More specifically, Cafaro’s recent research efforts involve: quantifying complexity with information geometry and statistical inference; applying geometric Clifford algebra techniques to classical electrodynamics and quantum computing; combatting quantum decoherence with quantum error correction schemes; employing statistical physics methods in complex network science.

Wenfeng Chen is a Lecturer in Applied Mathematics with a Ph.D. in Theoretical High Energy Physics and Mathematical Physics from the Chinese Academy of Science at Beijing. Dr. Chen has done research projects in Quantum Field Theory and Superstring/M-theory including topological ChernSimons theory, nonperturbative supersymmetric gauge Theory, Lorentz and CPT symmetry violation, AdS/CFT correspondence and gauge/gravity duality. Currently, Dr. Chen is focusing on applying the miscellaneous mathematical techniques of quantum field theory and string theory to study condensed matter physics, specifically nanophysics, and topological quantum computation.

Andrea Dziubek is an Assistant Professor of Applied Mathematics with a Ph.D. in Energy and Process Engineering from Berlin University of Technology, Germany. Her research interests include modeling and simulation of problems in biomedical engineering, continuum mechanics, shell theory, structure preserving numerical methods and finite element methods. She loves teaching subjects where she can share her academic interests and excitement, such as vector calculus and geometric mechanics, numerical mathematics, and partial differential equations.

Edmond Rusjan is an Associate Professor of Applied Mathematics with a Ph.D. in Mathematical Physics from Virginia Tech. His research focus is on geometry and symmetry inspired mathematical models. In particular, he has applied the Boltzmann equation, Lie groups and Lie algebras and CalabiYau spaces to solve problems in physics and engineering. He is currently studying the discretization of the Hodge star operator and implications for partial differential equations.

Tural Sadigov is a Lecturer of Applied Mathematics with a Ph.D. and M.S. in Applied Mathematics from Indiana University in Bloomington, Indiana. His research interests include Dynamical Systems and Partial Differential Equations focusing on determining parameters and determining forms for dissipative and semi-dissipative systems. He has been teaching various levels of Calculus, Probability, and Differential Equations. Currently, he is working on determining forms for subcritical Surface QuasiGeostrophic (SQG) Equations, and time series analysis with the main focus on ARIMA models.

William Thistleton is an Associate Professor of Applied Mathematics, with degrees in Electrical Engineering and Mathematics, and a Ph.D. in Applied Mathematics from SUNY Stony Brook. In addition to his extensive teaching experience in areas including Analysis, Computational Mathematics, and Data Analysis, he has scholarly publications in Probability and Statistics. He consults regularly with industry in a variety of settings.

Zora Thomova is a Professor of Applied Mathematics with Ph.D. in Mathematics from University of Montreal, Canada and M.S. in Engineering Physics from the Czech Technical University in Prague.

She is a recipient of the SUNY Chancellor’s Award for Excellence in teaching; her teaching experience includes mathematics courses at both the undergraduate and graduate level to mathematics and engineering students. She also teaches financial mathematics and fundamentals of derivative markets to MBA students and finance professionals at a major investment institution. She regularly publishes in the area of continuous symmetries of differential and difference equations and has served as an adviser on quantitative projects for financial clients.

SUNY Polytechnic Institute

Quad-C will offer internship and employment opportunities for current and future SUNY Poly students!

SUNY Poly offers a range of popular academic programs, many in fields with increasing demand taught by caring faculty. Advanced and

Certificate for Advanced Study (Post-Bachelor’s)

growing corporate partnerships on the Utica and Albany sites and

Computational Mathematical Modeling

across New York State provide the groundwork for internships and job opportunities. Small classes, quality programs, successful graduates, and an affordable investment are all reasons why we’re an increasingly popular choice among college-bound students.

For More Information: Graduate Admissions Office State University of New York Polytechnic Institute 100 Seymour Road Utica, NY 13502 315-792-7347 or toll free 1 (866) 278-6948 graduate@sunypoly.edu www.sunypoly.edu 8/16

SUNY Polytechnic Institute

Quad-C will offer internship and employment opportunities for current and future SUNY Poly students!

SUNY Poly offers a range of popular academic programs, many in fields with increasing demand taught by caring faculty. Advanced and

Certificate for Advanced Study (Post-Bachelor’s)

growing corporate partnerships on the Utica and Albany sites and

Computational Mathematical Modeling

across New York State provide the groundwork for internships and job opportunities. Small classes, quality programs, successful graduates, and an affordable investment are all reasons why we’re an increasingly popular choice among college-bound students.

For More Information: Graduate Admissions Office State University of New York Polytechnic Institute 100 Seymour Road Utica, NY 13502 315-792-7347 or toll free 1 (866) 278-6948 graduate@sunypoly.edu www.sunypoly.edu 8/16