Chair    James M. Tien
Associate Chair and Director, Master’s Programs   Charles J. Malmborg
Director, Undergraduate Program   Sunderesh S. Heragu
Director, Doctoral Program   Cheng Hsu
Department Home Page   http://www.rpi.edu/dept/dses/www/

As a result of a major strategic planning effort, Rensselaer recognized the need for educational programs in the decision sciences by the formation of a unique interdisciplinary Department of Decision Sciences and Engineering Systems in 1987. The objectives of the department are (1) to prepare engineers to design, develop, and implement complex decision-making systems and (2) to conduct research that leads to a better understanding of how information technology and quantitative analysis and modeling can support individuals, groups, and systems in problem solving and decision making. DSES achieves these objectives by extending and integrating knowledge from the disciplines of industrial engineering, information systems, operations research, mathematical statistics, computational intelligence/biotechnology, and systems engineering.

The Department of Decision Sciences and Engineering Systems offers programs in industrial and management engineering, manufacturing systems engineering, and operations research and statistics. Curricula in management engineering and/or industrial engineering have been offered since 1933. The interdisciplinary graduate program in operations research and statistics (OR&S) at Rensselaer was established in response to the rapid increase in the use of mathematical models for characterizing systems, understanding operations, and making decisions; both a master’s and a doctoral program were initiated in 1967. However, in 1988, the department replaced the OR&S Ph.D. with a unique Ph.D. degree in decision sciences and engineering systems, reflecting the focus of the department. The program in manufacturing systems engineering was inaugurated in fall 1992. This program is designed to emphasize modeling, statistical, computer, and management skills focused on the process of manufacturing. A common theme throughout all these programs is the use of mathematical, statistical, and computational/simulation models to better understand engineering, managerial, operational, and physical processes.

Areas of Advanced Research and Study

Manufacturing Systems   Faculty have developed methodologies and procedures for infrastructure and operating systems (e.g., production planning and control, scheduling and dispatching in flexible manufacturing systems), simulation of production facilities, manufacturing logistics, materials handling engineering, manufacturing facility design, information integration for design and manufacturing, control systems and agile manufacturing concepts for the electronics industry, and methodologies to integrate statistical quality control with computer graphics.

Service Systems   This area concentrates on the application of traditional and evolving industrial and systems engineering methodologies to the design and operation of service systems in both industry and the public sector. Areas of interest include production and inventory planning and control, distribution and logistics, facilities design, work design, quality assurance, intelligent transportation systems, and engineering economic analysis. Also included here is research in the deployment, allocation, and operation of urban service systems using computationally-based decision support methods.

Information Systems   Information and decision support systems have been developed and extensively used for disaster preparedness and management of disasters (e.g., searches for ships lost at sea, earthquakes) and manufacturing enterprises (e.g., manufacturing-driven design and scalable adaptive integration of databases over wide area networks). New theory and methodologies for internet-based information integration, E-commerce, data mining, and knowledge discovery are being developed. Decision support systems are being developed using a variety of knowledge engineering and computational intelligence tools. Additionally, methods, models, and technologies are being developed to aid in the planning and design of distributed information systems, information visualization, and user interfaces.

Mathematical Programming   Research topics include linear, nonlinear, integer, large-scale, multiple-objective, combinatorial, geometric, and stochastic programming. Of particular interest is research on the development and analysis of algorithms, computation, and the integration of uncertainty in optimization.

Statistics and Applied Probability   Research is conducted in the areas of data mining, knowledge discovery, and design of experiments—including optimality, efficiency, and robustness; nonlinear and robust estimation; statistical computing; probability; stochastic processes; queuing theory; reliability; quality control; and forecasting.

Simulation   Research topics in simulation are related to both modeling and analysis. They include development of automated simulation modeling and analysis tools; the use of artificial intelligence and graphical techniques; the validation of simulation models; and the development of both simulation systems and output analysis methods including simulation-based optimization.

Department Based Research Centers   While every department faculty member is involved in one or more of the following three research centers, several other faculty in the School of Engineering, as well as in the other four schools, are also participating in the activities of these centers.

Electronics Agile Manufacturing Research Institute   The Electronics Agile Manufacturing Research Institute (EAMRI) grew out of an initial federally-funded, five-year project focused on agile manufacturing information technologies as a strategy that can help the electronics manufacturing industry achieve its goals. Agile manufacturing concepts employ network-based information for supply chain oriented technologies and organizations as well as improved communications to help solve design and manufacturing problems. The EAMRI provides a national focus for developing and sharing methods to enable the U.S. electronics industry to adopt agile manufacturing. The EAMRI’s faculty include expertise in electronics design and manufacturing and are associated with engineering, computer science, and management disciplines. The EAMRI’s initial information technology, known as the Virtual Design Environment, has recently received a patent from the U.S. Patent Office.

Center for Services Research and Education   The goal of the Center for Services Research and Education (CSRE) is to enhance our understanding of the services sector and its functioning and to educate students and managers seeking careers in the services economy, which accounts for more than three-quarters of the U.S. gross national product. CSRE faculty were the first group to highlight the duality between services and manufacturing; many of the manufacturing methods are quite applicable to services systems and can be employed to enhance services productivity and competitiveness. The CSRE takes a holistic approach to the multi-faceted services sector and brings together experts from engineering, marketing, psychology, economics, and management policy and organization, among others. The experts examine the common elements which characterize all aspects of the services sector and develop generic principles that apply across the wide spectrum of services.

Rensselaer Statistical Consulting Center   The Rensselaer Statistical Consulting Center (RSCC) provides statistical planning and analysis services to Rensselaer researchers who require such services; consults with companies and government agencies which require advice on stateof- the-art statistical and probabilistic methods and their applications; allows graduate students, as part of their educational program, to apply—in a supervised manner—established and new statistical and probabilistic approaches to real-world problems; and offers general and organizationspecific, short-term training programs and state-of-the-art courses in statistical methodologies and practices. The Center’s faculty represent a range of statistical expertise, and they have extensive research and consulting experience. These faculty, together with talented graduate students, provide, on a consulting or short course basis, advice and guidance on the appropriate use of statistical and probabilistic methods.

Faculty

Professors

Berg, D.   NAE, Ph.D. (Yale University); management of technological organizations, innovation, policy, robotics, policy issues of research and development in the service sector.
Ecker, J.G.   (Mathematical Sciences) Ph.D. (University of Michigan); mathematical programming, multiobjective programming, geometric programming, mathematical programming applications, ellipsoid algorithms.
Grabowski, M.   Ph.D. (Rensselaer Polytechnic Institute); management information systems, knowledgebased systems, human and organizational error in large-scale systems, impact of information technology on systems and organizations; Research Professor.
Graves, R.J.   Ph.D. (State University of New York at Buffalo); manufacturing systems modeling and analysis, facilities planning and material handling system design, scheduling systems, concurrent engineering/design for manufacture, continuous flow manufacturing systems design, distributed manufacturing concepts and information infrastructure.
Haddock, J.   (Lally School of Management and Technology) Ph.D. (Purdue University); modeling of production and service systems including simulation and optimization techniques.
Hsu, C.   Ph.D. (Ohio State University); electronic commerce, metadatabase and information systems, enterprise integration and modeling, internet enterprises planning, computerized manufacturing, information visualization, and economic evaluation of cyberspace-augmented enterprises.
Hughes, G.   (Economics) Ph.D. (Princeton University); global economics, economics of information technology; Clinical Professor.
List, G.F.   (Civil Engineering) P.E., Ph.D. (University of Pennsylvania); real-time control of transportation network operations; multiobjective routing, scheduling, and fleet sizing; operations planning; hazardous materials logistics.
Malmborg, C.J.   Ph.D. (Georgia Institute of Technology); modeling and analysis of problems in facility design, materials handling, material flow, storage systems, simulation-based optimization methods, manufacturing systems, and decision analysis.
Raghavachari, M.   Ph.D. (University of California at Berkeley); statistical inference, quality control, multivariate methods, scheduling problems.
Simons, G.R.   (Lally School of Management and Technology) Ph.D. (Rensselaer Polytechnic Institute); industrial and management engineering, production and operations management, project planning and control, manufacturing systems.
Tien, J.M.   (Electrical, Computer and Systems Engineering) NAE, Ph.D. (Massachusetts Institute of Technology); systems modeling, queuing theory, public policy and decision analysis, computer performance evaluation, information and decision support systems, expert systems, computational cybernetics.
Wallace, W.A.   Ph.D. (Rensselaer Polytechnic Institute); decision support systems, the process of modeling, environmental management, disaster management.
Willemain, T.R.   Ph.D. (Massachusetts Institute of Technology); probabilistic modeling, data analysis, forecasting.

Associate Professors

Embrechts, M.J.   Ph.D. (Virginia Polytechnic Institute); application of neural networks and fuzzy logic for manufacturing and process control; image recognition and classification with the aid of neural networks; smart experiments; neural networks for trading and finance; neural networks, fractals, chaos, and wavelets for time-series analysis; data mining and computational intelligence.
Foley, W.J.   Ph.D., P.E. (Rensselaer Polytechnic Institute); engineering design, computer simulation modeling, health applications of operations research, health care policy analysis; Clinical Professor.
Heragu, S.S.   Ph.D. (University of Manitoba); artificial intelligence, cellular manufacturing, facilities design, intelligent manufacturing systems, materials handling, design of next generation factory layouts, production and operations management, operations research, scheduling, storage and warehousing.
Mitchell, J.E.    (Mathematical Sciences) Ph.D. (Cornell University); mathematical programming, integer programming, interior point methods, column generation methods, financial optimization, stochastic programming.
Sullo, P.   Ph.D. (Florida State University); reliability, life testing, statistical quality control, quality management, biostatistics, and industrial statistics.

Assistant Professors

Aboul-Seoud, M.   Ph.D. (University of Louisville); reliability engineering, quality control, and operations research.
Burke, R.J.   (Lally School of Management and Technology) Ph.D. (University of Massachusetts at Amherst); statistical quality control for critical mission components, design of experiments, statistical analysis, applied operations research.
Gupta, A.   Ph.D. (Stanford University); behavioral aspects of optimization and application problems in finance, large scale problems in decision making problems, methods and tools for solving large scale problems using simulation, simulation-based optimization.
Shang, N.   Ph.D. (University of California at Berkeley); nonparametric statistics; high dimensional data analysis; tree-structured methodology; statistical computing; discrete data analysis and survival/reliability data analysis.
Yang, Yingrui   (Philosophy, Psychology & Cognitive Sciences) Ph.D. (New YorkUniversity); cognitive psychology, thinking, reasoning, and decision-making, and cognitive science.

Lecturer

Reese, S.A.   B.S. (University of Iowa); e-business technologies.

Adjunct Faculty

Buttridge, J. J.   B.S. (Husson College); safety management, environmental safety and health, occupational safety, and hazardous waste management.
Kupferschmid, M.   P.E., Ph.D. (Rensselaer Polytechnic Institute); mathematical programming, algorithm performance evaluation, engineering applications.
Lawrence, C.   Ph.D. (Cornell University); statistical methods for bioinformatics, biometrics, Bayesian statistics, sequential analysis, statistical computing.
Mars, C.M.   B.S. (Rensselaer Polytechnic Institute); industrial safety and hygiene.
Sandhu, D.   Ph.D. (University of Toronto); stochastic models in operations research, complex queuing networks and applications to communication and manufacturing systems.

Affiliated Faculty

Professors

Desrochers, A.   (Electrical, Computer, and Systems Engineering) Ph.D. (Purdue University); performance modeling of automated manufacturing systems application to Petri nets, transfer lines, manufacturing architectures, database and network transactions, and distributed systems.
Grivas, D.   (Civil Engineering) Ph.D. (Purdue University); engineering infrastructure asset management systems; infrastructure databases; applications of fuzzy sets and expert systems; probabilistic modeling, risk analysis, assessment, and management.
Kelly, L.J.   (Rensselaer at Hartford) Ph.D. (University of Connecticut); statistics, operations.
Norsworthy, J.R.   (Lally School of Management and Technology) Ph.D. (University of Virginia); economics of productivity, productivity measurements, industrial economics.
Paulson, A.S.   (Lally School of Management and Technology) Ph.D. (Virginia Polytechnic Institute); risk management, financial models, multivariate statistics, time series and forecasting, survival data analysis.

Associate Professors

Bennett, K.   (Mathematical Sciences) Ph.D. (University of Wisconsin); mathematical programming, operations research, artificial intelligence.
Franklin, W.R.   (Electrical, Computer and Systems Engineering) Ph.D. (Harvard University); computational geometry, graphics, and CAD, cartography, parallel algorithms, large data bases, expert system verification.
Goldenberg, D.H.   (Lally School of Management and Technology) Ph.D. (University of Florida); derivatives markets, stochastic modeling of prices, options in corporate finance.
Gutierrez-Miravete, E.   (Rensselaer at Hartford) Ph.D. (Massachusetts Institute of Technology); materials processing, transport phenomena, clean technologies, advanced mathematics for applications, numerical computing, mathematical modeling and computer simulation.
Maleyeff, J.   (Rensselaer at Hartford) Ph.D. (University of Massachusetts); statistical quality assurance in manufacturing, administration and health care, computer simulation of operating systems, and development of effective teaching methodologies.

Assistant Professor

Arnheiter, E.D.   (Rensselaer at Hartford) Ph.D. (University of Massachusetts); Monte Carlo simulation and probabilistic models in quality, modular consortiums, automotive production models.
Ravichandran, T.   (Lally School of Management and Technology) Ph.D. (Southern Illinois University, Carbondale); management information systems.
Zaki, M.J.   (Computer Sciences) Ph.D. (University of Rochester); design of efficient, scalable, and parallel algorithms for various data mining techniques.

Undergraduate Program

Industrial and Management Engineering (IME) Curriculum   The first two years provide a strong foundation in basic science, engineering science, mathematics, and the humanities and social sciences. These two years are oriented toward the quantitative (mathematical) approach. Computer-based technology, including simulation, modeling, and systems design, is emphasized. In the last two years of the program, students concentrate on building expertise in statistics, operations research, manufacturing, and industrial engineering methods and models. Through the appropriate choice of electives, students can focus on their selected areas of interest. Design projects include problems in both manufacturing and service systems including information systems, health delivery systems, distribution and logistics, financial services, retail services, and public systems. It is advisable to develop a plan of study leading to the desired degree and concentration at or before the beginning of the third year.

The IME curriculum seeks to graduate high quality industrial and management engineers and to prepare them for successful careers in the 21st Century. Such careers require graduates to have the ability to apply knowledge of mathematics, science, and computing; to analyze and interpret data; to identify, formulate, and solve problems; to understand the impact of engineering solutions in a global and societal context; to approach a problem from a total integrated systems perspective; to apply knowledge of manufacturing and service systems in managing people and systems to achieve and maintain the efficient use of resources; to communicate effectively and function as a leader on multidisciplinary teams; and to actively engage in lifelong learning.

Dual Major Programs   Dual major programs lead to a single baccalaureate degree embracing two disciplines. Special programs that can be completed in eight semesters have been developed. Examples include dual majors in: industrial and management engineering and aeronautical engineering, industrial and management engineering and civil engineering, industrial and management engineering and computer and systems engineering, industrial and management engineering and mechanical engineering. Detailed information about these programs is available in the department curriculum office.

Cooperative Education Program   This strongly recommended option allows students to gain professional experience as part of the educational program. The faculty adviser and Career Development Center can assist in planning individual study-work schedules

Baccalaureate Program   It is recommended that students who decide to major in industrial and management engineering declare their intent as early in their academic career as possible. Students who decide to join the IME program after the sophomore year risk delaying completion of their undergraduate studies. A typical four-year program is presented below. It is recommended that students work with their assigned faculty advisers closely to ensure that all degree requirements are satisfied.

1. Students may take any approved four-credit course in science, computer science, or mathematics.
2. Students must select any two of the following approved multidisciplinary electives: ENGR-1600 Chemistry of Materials II ENGR-2530 Strength of Materials ENGR-2090 Engineering Dynamics ENGR-4050 Modeling and Control of Dynamic Systems ENGR-2250 Thermal and Fluid Engineering I ENGR-4300 Electronic Instrumentation
3. This course can be fulfilled by taking a 2-credit course from a list of courses published at the start of each semester.
4. Students may select any two of the following courses to satisfy technical elective requirements: DSES-4200 Design and Analysis of Work Systems DSES-4260 Industrial Safety and Hygiene DSES-4250 Facilities Design and Industrial Logistics DSES-4810 Computational Intelligence DSES-4960 Decision-Focused Systems Engineering
5. May be taken in either fall or spring semester.

Minimum Credit Hours   This curriculum requires a minimum of 125 credit hours and completion of the course requirements listed above.

Humanities or Social Sciences Electives   The humanities and social sciences electives are based on the Institute and School of Engineering requirements for these electives. It is recommended that the student elect sequences in appropriate departments in order to provide adequate breadth and depth in subject areas. Students desiring minors must consult the school or department in which these courses are offered for specific requirements.

Electives   Electives may be chosen from any academic discipline to broaden the student’s educational background and/or develop greater depth in a selected discipline. Professional Program Students can design a plan of study leading to a professional Master of Engineering degree in Industrial and Management Engineering in addition to the bachelor’s degree. Students should consult with their advisers regarding the design of such a program as soon as they make a decision to pursue the professional master’s degree.

Graduate Programs

Industrial and Management Engineering Curricula   The Department of Decision Sciences and Engineering Systems offers two master’s degrees in industrial and management engineering. These include the Master of Science degree that requires a master’s project or thesis and a non-thesis Master of Engineering degree.

The master’s program in Industrial and Management Engineering is available through distance learning. Contact the Rensselaer Satellite Video Program (RSVP) office for course scheduling information.

Admissions Requirements   All applicants must take the Graduate Record Exam (GRE); this is especially important for those requesting financial aid due to the large number of aid requests. The GRE may be waived for students applying to the Master of Engineering program. In special situations, and with departmental permission, the GMAT may be substituted for the GRE.

Prerequisite Courses   All students seeking the Master of Engineering or Master of Science degree in Industrial and Management Engineering must satisfy the following two courses in their undergraduate program at Rensselaer or have had the equivalent courses elsewhere:

• Applied Operations Research (equivalent to DSES-4640 or, if the student is seeking an Applied Operations Research concentration, a course equivalent to DSES-4770 must be taken);
• Introduction to Applied Statistics (equivalent to DSES-6110)

The master’s degrees in industrial and management engineering are both a minimum of 30 credit hours. Students who have taken at least one of the above prerequisite courses (or their equivalents) prior to entering the program will follow a 30 credit hour program of study. Students who have previously taken none of the above prerequisite courses will follow programs of study of 33 credit hours.

Core Courses   In addition to the above prerequisite courses, a student’s core course work must include:

Master’s Thesis or Project   Students seeking the Master of Science degree option must also complete three to nine credit hours of DSES-6990 (Master’s Thesis) or DSES-6980 (Master’s Project). The thesis or project credits can also count toward the nine credit hour (3-course) concentration requirement described below.

Concentration Courses   The remainder of the program can be tailored to the student’s interest. The plan of study must include a concentration area, which is defined as a set of three or more courses (or nine credit hours) that reflects a logical progression for developing a base of expertise in an area of study. Concentrations will usually, but not always, include at least one of the core courses listed above. Several concentration areas and acceptable courses applicable to each area are listed below:

* Students can also satisfy this part of the requirement for the service systems concentration through two application-focused courses approved by the academic adviser. Examples of applications foci include financial services, health systems, transportation, retailing, public systems, quality systems, and marketing.

Although it is possible for a well-prepared student to complete the requirements for the master’s degree in one academic year, it is recommended that the student allow a full calendar year or three academic semesters to complete the program of study.

Manufacturing Systems Engineering Curricula

The Department of Decision Sciences and Engineering Systems (DSES) offers multidisciplinary Master of Science and Master of Engineering degrees in Manufacturing Systems Engineering. This program combines modeling, statistical, computer, design, and management skills focused on the process of manufacturing. The program provides students with an option to combine core courses in DSES with concentration courses in electrical engineering, mechanical engineering, computer science, or management. Alternatively, students can elect to focus the entire plan of study in DSES. Course work concentrations are available in manufacturing systems modeling, manufacturing information systems, manufacturing systems quality, manufacturing processes and technology, and manufacturing systems management.

Admission Requirements   All applicants are encouraged to take the Graduate Record Exam (GRE); this is especially important for those requesting financial aid, due to the large number of aid requests. The GRE may be waived or the GMAT may be substituted for the GRE for students applying to the Master of Engineering program.

Prerequisite Courses   All students seeking the Master of Science or Master of Engineering degree in Manufacturing Systems Engineering must satisfy the following two courses in their undergraduate program at Rensselaer or have had the equivalent course work elsewhere:

DSES-4640 Operations Research I (or equivalent)
DSES-6110 Introduction to Applied Statistics

The Master of Science and the Master of Engineering degree in Manufacturing Systems Engineering are a minimum of 30 credit hours. Students who have taken at least one of the above prerequisite courses (or their equivalents) prior to entering the program will follow a 30 credit hour plan of study. Students who have previously taken none of the above prerequisite courses will follow a program of study of 33 credit hours.

Core Courses   In addition to the above prerequisite courses, a student’s core course work will include:

Master’s Thesis or Project   Students seeking the Master of Science degree option must also complete three to nine credit hours of DSES-6990 (Master’s Thesis) or DSES-6980 (Master’s Project). The thesis or project credits can also count toward the nine credit hour (3-course) concentration requirement described below.

Concentration Courses   The remainder of the program can be tailored to the student’s interest. The plan of study must include a concentration area, which is defined as a set of three or more courses that reflects a logical progression for developing a base of expertise in an area of study. All concentrations include one of the core courses listed above. The five concentration areas of the program and the courses applicable to each area are listed below:

Elective Courses   For most students, the Master of Engineering program of study will allow for six credit hours of elective courses. Although it is possible for a well prepared student to complete the requirements for the Master of Engineering degree in one academic year, it is recommended that the student allow a full calendar year or three academic semesters to complete the program of study.

Operations Research and Statistics Curricula

The Department of Decision Sciences and Engineering Systems offers two master’s degrees in Operations Research and Statistics. These include the Master of Science degree that requires a master’s project or thesis and a non-thesis Master of Engineering degree. Both programs combine course work in applied probability and statistics, optimization, modeling, and decision sciences in the scientific application of quantitative tools to support decision making. The scope of the programs includes the formulation, solution, and implementation of mathematical models of decision problems to measure, evaluate, and optimize system performance. Most students can complete either program in 30 credit hours. Any student planning to enroll in the DSES doctoral program should consult closely with his or her faculty adviser prior to making course selections.

Programs of Study   The faculty’s diversity enables these programs to encompass varied research topics. A student may work simultaneously on a master’s degree in this program and on a degree in computer science, business administration, mathematics, computer and systems engineering, or another related area.

Degree Requirements   Most students in the program hold a bachelor’s or master’s degree in engineering, mathematics, the physical sciences, business administration, or management. Students with training in other disciplines, such as economics and the social sciences are also encouraged to apply if their quantitative backgrounds include the equivalent of at least three semesters of calculus and linear algebra.

Admission Requirements   All applicants are required to take the Graduate Record Examination (GRE) except under extenuating circumstances. Prerequisite Courses There are no prerequisites for the master’s programs in operations research and statistics other than knowledge of calculus and linear algebra.

Required Courses   The following courses or their equivalents are required:

• DSES-4640 Operations Research I—4 credit hours and
• DSES-4750 Probability Theory and Applications—4 credit hours and
• DSES-4760 Mathematical Statistics—4 credit hours and
• DSES-6050 Stochastic Processes—3 credit hours or
• DSES-6150 Advanced Probability for Statistical Inference—3 credit hours or
• DSES-6820 Queuing Systems and Applications—3 credit hours and
• DSES-6620 Simulation Modeling and Analysis—3 credit hours
• Computational Data Analysis Elective *—3 credit hours
• Modeling/Optimization Elective**—3 credit hours

*The computational data analysis elective can be satisfied by taking either DSES-6060—Applied Multivariate Analysis, DSES-6130 Statistical Computing or DSES-6140 Exploratory Data Analysis.
**Students electing the modeling/optimization concentration are required to take DSES-4770—Mathematical Models of Operations Research in place of DSES-4640.

Concentration Courses   The remainder of the program can be tailored to the student’s interest. The plan of study must include a concentration area, which is defined as a set of three or more courses that reflects a logical progression for developing a base of expertise in an area of study. In addition, candidates for the Master of Science degree must complete a master’s thesis or project and register for 3-6 credits of DSES-6990 or DSES-6980, respectively. Several concentration areas and acceptable courses applicable to each area are listed below:

Doctoral Degree in Decision Sciences and Engineering Systems

Students may choose to major in industrial engineering, information systems, manufacturing systems engineering, operations research, or statistics. Advanced research and a dissertation in the chosen field are required in the doctoral program.

Requirements

• Institution requirements of the Graduate School.
• Seminar in DSES Research: Doctoral students must register for and complete one semester of DSES-6900 during the fall semester of the first academic year of residency. This course is intended to introduce the student to the research environment at Rensselaer and to provide background on the process of doctoral research in DSES. The course is also aimed at developing the student’s communication skills.
• Doctoral Qualifying Examination: Each student must take a five-hour written examination covering the core areas of DSES. The basis for this examination includes DSES-4530, DSES-4750, DSES-4760, DSES-4770, and DSES-6500 or their equivalents. This examination is normally taken at the end of the second semester of residency. A more detailed description of this and other required examinations is available from the DSES doctoral program director.
• Doctoral Area and Advanced Seminar Requirement: Doctoral students must register for and complete DSES-6910 Advanced Seminar in DSES Research during the fall semester of the second academic year of residency. This seminar reviews methods for undertaking DSES research and requires students to prepare a research paper under the guidance of a faculty adviser. The Area Requirement also includes course work in a selected major area. See the DSES doctoral program director for detailed documentation of DSES Doctoral Area Requirements.
• Doctoral Candidacy Examination: Each student must take an oral candidacy examination after passing the Area Requirement but before completing 75 credit hours of graduate work. This examination tests the candidate’s background for the proposed research, appropriateness of the proposed research, and the ability of the candidate to successfully complete the research. The thesis research proposal presented by the candidate must contain at least one result that meets journal publishing standards.
• Doctoral Dissertation and Defense: Each student must write a doctoral thesis and publicly defend the thesis in a formal oral defense.

Course Requirements   Apart from the Seminar and Advanced Seminar in DSES Research, Doctoral Area course requirements, and equivalent course material for the Doctoral Qualifying Examination, there are no formal course requirements for the doctoral degree. However, the student is expected to develop in-depth knowledge in his/her dissertation area through appropriate course work as well as supervised research. A plan of study is required, which must be approved by the thesis adviser and the DSES doctoral program director. Representative programs of study are available from the DSES doctoral program director.

Courses   Courses in Decision Sciences and Engineering Systems are described in this catalog under DSES.