Rensselaer Catalog 20152016 [Archived Catalog]
Mathematical Sciences


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Head: Donald Schwendeman
Associate Head: Bruce Piper
Chair of the Graduate Committee: John Mitchell
Departmental Home Page: http://www.math.rpi.edu/index.html
Through the centuries, mathematics has been a central feature of our intellectual and technological development. Today its role in the physical sciences and engineering is well established. Its role in the life and social sciences, medicine, management, and the arts is undergoing remarkable growth—a virtual mathematization of the culture. The Department of Mathematical Sciences is directly engaged in this process through its educational and research programs. Our focus is the study and development of mathematical and computational methods and their application to problems of contemporary significance to our society.
The Department of Mathematical Sciences provides an indepth education in both the foundations of mathematical thought as well in the applications of mathematics to reallife phenomena. For this reason, we offer a baccalaureate degree with a specialization in mathematics, applied mathematics, mathematics of computation, or operations research. The department’s programs are also designed to provide a broad spectrum of opportunities for students. This flexibility allows students and advisers to tailor programs to individual objectives and talents. As a result, the curricula are equally advantageous for individuals who will seek immediate employment upon graduation, for those who plan graduatelevel education in the mathematical sciences, and for those who will apply their education to pursuits outside the mathematical arena. Department graduates have entered careers in law, medicine, engineering, management, and psychology, as well as in pure and applied mathematics, computer science, and operations research.
At the graduate level, Rensselaer is especially wellknown as a center for advanced study and research in applied mathematics. The department’s M.S. and Ph.D. programs emphasize:
 methods of applied mathematics, including ordinary and partial differential equations, approximation theory, asymptotic analysis, functional analysis, and numerical analysis;
 applications in the physical sciences, biological sciences, and engineering;
 computational mathematics, including the development, analysis, and implementation of numerical methods for mathematical models that arise in applications;
 mathematical programming, including nonlinear, combinatorial, and multiple objective optimization and their applications.
At the highest level, continual interplay between the construction of the mathematical model and the solution of the resulting mathematical problem characterizes applied mathematics. The ideal applied mathematician, therefore, must be knowledgeable both in mathematics and in at least one field in which problem areas are found. A sound knowledge of the application area assists in constructing suitable models, and a high level of mathematical judgment and expertise may be required to solve the resulting mathematical problems.
Research Innovations and Initiatives
Faculty research activities in the Department of Mathematical Sciences center on applied mathematics, analysis, scientific computing, mathematical programming, and operations research. The faculty’s interest in applied research often leads to a synthesis of techniques from two or more research areas. Further, the formulation, solution, and interpretation of a problem often contain ideas that can be applied to problems in other areas. Focusing different research areas on real problems and the diversity of applications of real problem solutions creates an atmosphere of interaction and cooperation within the department and the university, as well as with other major research institutions.
Numerical Analysis and Scientific Computation
Investigations range from the study of fundamental problems in linear algebra to the development and analysis of numerical methods for solving particular physical or life science problems. Research activities include the numerical solution of optimization problems, inverse eigenvalue problems, and free boundary problems; finite difference and finite element methods for stiff initial and boundaryvalue problems; and methods of resolving problems involving composite materials. Applications of these studies include reacting flows, shockwave propagation, semiconductor performance, biomathematics, acoustic signal propagation, and incompressible flow in various geometries.
Inverse Problems
This research involves the recovery and imaging of internal biological, mechanical, electrical, or magnetic properties of a system from boundary, spectral, scattering, or internal data. The physical system is modeled by a differential equation system with specific unknown terms representing, for example, stiffness in an elastic system or electric permittivity in an electromagnetic system. The measured data is indirectly related to the unknown quantities so the mathematical model of physical processes that produces the data plays an important role in the recovery of the unknown quantities. Rensselaer has established a center for Inverse Problems at RPI. Current research applies functional analysis, perturbation theory, numerical analysis, and optimization to determine optimal datasets, to study the nonlinear dependence of the unknown physical quantities on the available data, and to obtain approximations of the nonlinear operators that will yield efficient reconstruction algorithms. There is a significant role for modeling, analysis, scientific computation, and algorithm development to obtain the targeted solutions to these problems.
Dynamical Systems
Dynamical systems are used to model timeevolving processes from physics to engineering and biology, which frequently require large numbers of variables for their description, and evolve in complex, unpredictable ways. Using ordinary, partial, and stochastic differential and difference equations, as well as deterministic and stochastic numerical simulations, investigations are made into models of phenomena ranging from internal waves in the ocean, vortex dynamics, and turbulence, through largescale neuronal and social networks, innerear dynamics, sleep cycles, fluid flow stability, detonations, and shock dynamics.
Wave Propagation
These studies focus on determining wave propagation and understanding the influence of wave effects. A major area of interest is ocean acoustic transmissions. Mathematical models are developed and analyzed to describe acoustic consequences of ocean environmental features (such as largeamplitude internal waves, ocean current and frontal systems and sediment variations) on the propagation of signals in both frequency and time domains, and to improve the accuracy of computational methods. Improved numerical and asymptotic methods are derived and tested, providing new ways to extract information in complex propagation environments. Stochastic propagation effects are modeled and analyzed, and results are used to explain variability in data observed by ocean scientists. Results are generalized and applied to other acoustic propagation environments, ranging from the atmospheres of Jupiter and the Earth to the upper layer of the Earth’s crust.
Mathematical Programming and Operations Research
Mathematical programming endeavors to find optimal solutions for a broad range of problems including medical, financial, scientific, and engineering problems. Research is conducted on the development, evaluation, and comparison of serial and parallel algorithms for a variety of mathematical programming problems. Current research topics include interior point methods for linear, integer, and nonlinear programming; branchandbound and branchandcut approaches to integer programming problems; column generation methods; financial optimization; and mathematical programs with complimentary constraints. Also under investigation are mathematical programming approaches to problems in artificial intelligence such as machine learning, neural networks, support vector machines, pattern recognition, and planning. This research also considers combining operations research and artificial intelligence problemsolving methods, scalability of these methods to large problems in data mining, mathematical programming approaches to other areas in computer science such as database query optimization, and stochastic programming.
Biomathematics
Mathematical biology is a very active area of applied mathematical research. This is an interdisciplinary endeavor, with a strong interaction with biological and biomedical scientists. Projects of current interest include cardiac imaging and the use of computer graphics to construct pictures of the heart, mechanoreception, mathematical modeling of biological systems that transform mechanical stimuli (e.g., sound, touch, etc.) into ionic or neural signals and molecular systems in cells. Also being studied are nonlinear ionic diffusion in polyelectrolytic gels and the mechanics of multiphasic tissues like cartilage and the cornea. Numerical analysis, asymptotics, and functional analysis are used to investigate mathematically posed problems resulting from the models.
Fluid Mechanics
Methods of applied mathematics are being used to study how fluids behave under a wide spectrum of conditions. The physical problems treated, which are multiscaled, usually lead to partial differential equations, which may be linear or nonlinear. Current problems deal with fluid mechanics in engineering systems, the flow and stability of twophase mixtures, the transition from laminar to turbulent flow in boundary layers, and the evolution of nonNewtonian (e.g., polymer) fluid flow. Other geophysical initiated flows such ocean interacions of nonlinear internal waves and acoustics are being investigated.
Combustion Theory
Investigations include mathematical modeling of combustion and explosion phenomena, and analytical and computational studies of the resulting systems of nonlinear ordinary and partial differential equations. Topics of interest are evolution and interaction of waves in reactive media, combustion and vortex breakdown in swirling flows, and transition from deflagration to detonation in granular explosives. Recent studies have focused on the development and analysis of multiphase and multiphysics models of confined granular explosives, both from the micro and the macro points of view.
Geometric Design
In many design and animation applications, the appearance of objects and shapes are of primary importance. There are a variety of mathematical properties of curves and surfaces that correspond to visual aesthetics. Research in this area uses mathematical techniques from differential geometry, computational geometry and optimization to create and analyze schemes for curve, surface, and animation design.
Complex Systems
The study of complex systems can be loosely characterized as the investigation of phenomena that arise in systems with many nonlinearly interacting degrees of freedom, with modern examples drawn from the study of lasers, nonlinear and fiber optics, waves and turbulence in fluids, molecular and cellular biology, and neuroscience. The mathematical treatment of these systems generally involve two themes: a statistical or stochastic characterization of some degrees of freedom, and the development of systematic links between the rules of nonlinear interaction between individual elements with the effective collective behavior of the system. Among the tools employed for this latter objective are homogenization theory, weak turbulence theory, and various stochastic mode reduction and averaging procedures.
Bioinformatics
The massive volume of new data being produced by genome sequencing projects point to an increasing need for bioinformatics. This is a highly interdisciplinary field, involving faculty in mathematical sciences, biology, computer science, chemistry and several departments in the School of Engineering. Rensselaer has established a joint bioinformatics center with the nearby Wadsworth Laboratories in the New York State Department of Public Health. Current activities at Rensselaer comprise the development and application of algorithms that aim to solve biological problems using DNA and amino acid sequence, structure, and related information. Some of the problems addressed are the search for patterns in biomolecular sequences that are functionally important, such as transcription binding sites; the prediction of structure or function from nucleic acid or protein sequence data; the development of methods and databases to classify large amounts of biological information, and the development of algorithms and software that are important for current biotechnology applications.
Undergraduate Programs
Mathematics has always been the cornerstone of scientific development. Rensselaer’s aim is to provide an education in mathematics, both as a subject in itself and as a discipline to aid in the development of other social and scientific fields. The undergraduate mathematics program educates students in a variety of mathematical areas. The flexibility in this program, with its numerous options, permits selection of courses ranging from pure theory (which builds a foundation for more advanced studies), to applied subjects focusing on mathematical modeling and the solution of realworld problems. In particular, Rensselaer’s Department of Mathematical Sciences is one of the few American programs with a strong faculty orientation toward mathematics applications. Reflecting this emphasis are the many undergraduate courses dealing with areas of mathematical applications and the applied flavor with which department faculty typically teach them.
Outcomes of the Undergraduate Curricula
Students who successfully complete this program will be able to demonstrate:
 skills in the use of basic elements of mathematics and mathematical modeling.
 an ability to write mathematical proofs and an appreciation of the rigorous and logical structure of mathematics.
 an ability and confidence to selflearn new mathematical concepts.
 an ability to apply critical and logical thought, and mathematical principles, to diverse problems.
 specialized knowledge of advanced mathematics in one or more of the tracks of Analysis and Algebra, Mathematics of Computation, Applied Mathematics, or Operations Research.
Baccalaureate Programs
Four curricula leading to a B.S. in Mathematics have been designed to permit the construction of programs that reflect individual student interests and career objectives. These curricula include:

Mathematics—a traditional program emphasizing the elements of pure and applied mathematics.

Applied Mathematics—emphasizing both the modeling of physical phenomena and methods of analyzing the resulting mathematical problems.

Mathematics of Computation—a program bridging mathematics and computer science, with emphasis on numerical methods for solution of problems in science and engineering.

Mathematics of Operations Research—emphasizing the use of mathematics in developing and studying analytical models of discrete systems, especially those that arise in management, engineering, and social sciences.
These four curricula share several common features. First, they each contain eight free electives that permit students to design unique programs. These electives also allow students to concentrate on a subject in addition to mathematics, to obtain a broadbased education, or to complement their mathematics program. A second common feature is the Humanities, Arts, and Social Sciences requirement of 24 credits. Finally, completion of all four curricula requires a total of 124 credits.
An immediate choice among these four curricula is not necessary, since for the first two years, all mathematics students follow the same basic curriculum. This initial twoyear course of study is outlined in the Programs section of this catalog and is followed by sample junior/senior curricula for each of the department’s four undergraduate programs. In addition to the specific requirements in each track, it is strongly recommended that students planning to pursue graduate study in mathematics take the following courses:
MATH 4100 Linear Algebra
MATH 4200 Mathematical Analysis I
MATH 4210 Mathematical Analysis II
MATH 4300 Introduction to Complex Variables
Dual Major Programs
The requirements for a dual major are described in the section on Academic Information and Regulations. Interest in such programs is increasing, and recent combinations have included math and physics, math and computer science, and math and psychology. Typical schedules for such combinations can be found at the department’s Web site under dual majors.
Accelerated Programs
Qualified students may earn a B.S. and M.S. degree in the same or different areas in a shorterthanusual time. They may do so through the use of advanced placement credit, by taking additional courses during the fall and spring semesters, and/or by taking summer courses.
For example, a student with advanced placement credit for Calculus I and II may earn the B.S. and M.S. degrees within four years by taking an additional course each regular fall and spring semester. Since a student may take up to 21 credit hours per semester at no additional charge, it may be possible to earn both degrees for the cost of a B.S. alone. As a second example, rather than taking more courses during the academic year, a student may earn two degrees in four years by taking eight courses distributed over three summers.
Such a joint degree program requires that the student apply to and be accepted by the Office of Graduate Education at an appropriate stage. A wide variety of joint degree programs can be arranged depending on the student’s background, interests, and desired rate of progress. The interested student should consult the faculty adviser to design an optimum program.
Graduate Programs
The Department of Mathematical Sciences offers programs leading to the M.S. and Ph.D. degrees. Each curriculum is highly flexible, and each student’s program of study is individually designed.
A departmental colloquium series, in which both mathematics faculty and guest lecturers present current research work, supplements course work. In addition, graduate students organize a weekly seminar, in which they present material from their research. Moreover, each semester, faculty and students organize informal seminars that explore topics of mutual interest. In a special course called Introduction to Research in Mathematics, each week a faculty member discusses his or her research program and describes current problems for graduate students to investigate. In addition, through formal course work and individual contact with the faculty, students become familiar with all departmental research activities. The department’s Web site also provides an overview of these research activities and lists the faculty working in each area.
Undergraduates with backgrounds in mathematics or any related major with significant mathematical content are admissible to the graduate program.
Course Descriptions
Courses directly related to all Mathematical Sciences curricula are described in the Course Description section of this catalog under the department code MATH or MATP.
Faculty*
Professors
Banks, J.—Ph.D. (Rensselaer Polytechnic Institute); numerical methods for partial differential equations, fluidstructure interaction, computational fluid dynamics and solid mechanics, scientific computing, wave phenomenon, laser plasma interaction.
Bennett, K.P.—Ph.D. (University of Wisconsin); mathematical programming, operations research, machine learning, data mining, artificial intelligence, applications of these methods to problems in engineering, business, medicine, biology, chemistry, and public health.
Boyce, W.E.—Ph.D. (Carnegie Institute of Technology); applied mathematics, mathematics education (emeritus).
Drew, D.A.—Ph.D. (Rensselaer Polytechnic Institute); applied mathematics, fluid mechanics, mathematical biology (emeritus).
Ecker, J.G.—Ph.D. (University of Michigan); mathematical programming, multiobjective programming, geometric programming, mathematical programming applications, ellipsoid algorithms, linear and nonlinear bilevel programming.
Habetler, G.J.—Ph.D. (Carnegie Institute of Technology); functional analysis, numerical analysis (emeritus).
Henshaw, W. D.—Ph.D. (California Institute of Technology); scientific computing, applied mathematics.
Herron, I.—Ph.D. (Johns Hopkins University); applied mathematics, fluid mechanics, hydrodynamics, stability, biometrics.
Holmes, M.—Ph.D. (University of California, Los Angeles); perturbation methods, biomathematics, nonlinear continuum mechanics.
Isaacson, D.—Ph.D. (New York University); mathematical physics, biomedical applications.
Jacobson, M.J.—Ph.D. (Carnegie Institute of Technology); applied mathematics, acoustic and electromagnetic wave propagation (emeritus).
Kapila, A.—Ph.D. (Cornell University); applied mathematics; fluid mechanics; multiscale, multiphase, and multiphysics problems; scientific computation.
Kovacic, G.—Ph.D. (California Institute of Technology); applied mathematics, nonlinear dynamics, nonlinear optics.
Kramer, P.R.—Ph.D. (Princeton University); turbulent diffusion, stochastic processes.
Lai, R.—Ph.D. (University of California, Los Angeles); scientific computing, optimization and numerical PDEs, computational differential geometry, manifold processing and applications to data science, Inverse problems, image processing and applications to medical image analysis.
Lim, C.C.—Ph.D. (Brown University); network sience, nonequilibrium statistical physics, applied probability and stochastic process, vortex dynamics and quasi2D fluid flows.
Lvov, Y.—Ph.D. (University of Arizona); mathematical physics and nonlinear phenomena.
McLaughlin, H.W.—Ph.D. (University of Maryland); applied geometry.
McLaughlin, J.R.—Ph.D. (University of California, Riverside); inverse bioelasticity problems, inverse vibration and inverse scattering problems, wave propagation, analysis, applied mathematics.
Mitchell, J.E.—Ph.D. (Cornell University); optimization, interger programming, conic optimization, mathematical programs with complimentary constraints, column generation methods, stochastic optimization, interdependent infrastructure, humanitarian logistics.
Siegmann, W.L.—Ph.D. (Massachusetts Institute of Technology); applied mathematics, wave propagation.
Schwendeman, D.W.—Ph.D. (California Institute of Technology); applied mathematics, scientific computing.
Zuker, M.—Ph.D. (Massachusetts Institute of Technology); bioinformatics.
Associate Professors
Li, F.—Ph.D. (Brown University); numerical analysis and scientific computing; finite element methods; discontinous Galerkin methods; numerical methods for Maxwell equations, Maxwelleignproblems, conservation laws, HamiltonJacobi equations, magnetohydrodynamics equations.
Piper, B.R.—Ph.D. (University of Utah); computeraided geometric design, numerical analysis, computer graphics.
Lecturers
Boudjelkha, M.—Ph.D. (Rensselaer Polytechnic Institute); ordinary and partial differential equations, special functions, RiemannBessel functions, asymptotoics.
Kiehl, M.—Ph.D. (Rensselaer Polytechnic Institute); biomathematics.
Kucinski, G.—Ph.D. (Binghamton University)
Schmidt, D.A.—Ph.D. (Rensselaer Polytechnic Institute); graph theory, qualitative matrix analysis, mathematics education.
Joint Appointment with Computer Science—Professor
Rogers, E.H.—Ph.D. (Carnegie Institute of Technology); VLSI architecture, computer applications (emeritus).
* Departmental faculty listings are accurate as of the date generated for inclusion in this catalog. For the most uptodate listing of faculty positions, including endofyear promotions, please refer to the Faculty Roster section of this catalog, which is current as of the May 2015 Board of Trustees meeting.
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