Systems Science Program

Binghamton’s Systems Science graduate program is now officially registered by the New York State Education Department as a Distance Education program. You can complete your advanced degree in Systems Science (Master’s, PhD) fully online from anywhere in the world. Apply today!!

The COVID-19 pandemic has made the whole world increasingly more aware of the relevance of Systems Science, its concepts and methodologies. How rapid and counter-intuitive is the exponential growth? Why are mathematical models and computer simulations important and helpful for real-world problem solving? How do social networks play a key role in contagion processes? Why can social distancing change the epidemic property of our social system? How critical are supply chain and healthcare systems management for saving lives in emergency situations? All of these are Systems Science questions. The abilities and skills to understand, model, predict and manipulate complex systems are more important than ever. 

We, the Systems Science program at Binghamton University, are committed to disseminating those critically needed systems skills for a wide variety of professional and academic workforces for the betterment of our global society. We offer highly unique, internationally recognized, transdisciplinary learning and research experiences. Join us to make a difference to the world.

Join us to learn more at an upcoming information session!

Programs Offered

• PhD in Systems Science (on-campus and online)
• MS in Systems Science (on-campus and online)
• MS in Systems Science with Health Systems concentration (on-campus and online)
• MS in Systems Science: Executive Health Systems concentration in Manhattan
• MPA/MS in Systems Science
• Advanced Graduate Certificate in Complex Systems Science and Engineering (available for all majors)
  

Selected Research Topics

Required Course Descriptions, Electives

Most of the courses are available fully online via Watson College’s EngiNet distance learning service.

Master's in Systems Science (MS SS)

Students must complete four required courses while maintaining at least a B average. Students will choose to complete either SSIE-520 or SSIE-523. 

• SSIE 500 - Computational Tools

  This course will introduce students to several programming languages and basic programming techniques, with the focus on developing practical code-writing skills for scientific/engineering problem solving. Topics to be covered include: manipulation with numbers, strings, variables, lists, and arrays; creating functions; flow control; data manipulation; imperative, functional, and object-oriented programming; visualization; and presentation. LaTeX will also be introduced for typesetting professional technical documents. This course will also discuss information theory as a sample application area of computational tools. Topics include: information and entropy, mutual information, information coding and compression, Markov information source model, statistical complexity, and computational complexity. Students will write codes in their preferred language to calculate various information theoretic measurements of real-world data. Prerequisite: Graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 501 - Intro to Systems Science

  Includes a general characterization of systems science as a field of study; intellectual roots, philosophical assumptions and historical development of the field; an overview of fundamental systems concepts, principles and laws; and a survey of application areas of systems science and its implications for other fields of study. Prerequisite: Graduate standing or permission of instructor. Crosslisted with ISE 440. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 505 - Applied Probability & Statist.

  Basic concepts in probability and statistics required in the modeling of random processes and uncertainty. Bayes' formula, Bayesian statistics, independent events; random variables and their descriptive statistics; distribution functions; Bernoulli, Binomial, Hypergeometric, Poisson, normal, exponential, gamma, Weibull and multinomial distributions; Chebyshev's theorem; central limit theorem; joint distributions; sampling distributions; point estimation; confidence intervals; student-t, x squared and F distributions; hypothesis testing; contingency tables, goodness of fit, non-parametric statistics, regression and correlation. Prerequisite: one year of calculus. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 520 - Modeling And Simulation

  Stochastic processes, review of probability and statistics, covariance, input data selection, random number generators, non-parametric tests for randomness, generation of random variates, output data analysis, terminating and non-terminating simulations, model validation, comparison of alternatives, variance reduction techniques, sensitivity analysis, experimental design and predictive models. Prerequisite: SSIE 505 or equivalent. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 523 - Collective Dyn of Complex Syst

  Introduces students to the study of collective dynamics demonstrated by various natural, social and artificial complex systems, i.e., systems made of a massive amount of lower-level components interacting with each other in a nonlinear way. Discusses several computational modeling frameworks, including agent-based models (particle models, ecological and evolutionary models, game-theoretic models), complex network models (small-world and scale-free networks, dynamical networks, adaptive networks), and spatial models (cellular automata, partial differential equations). Also discusses mathematical concepts and tools to analyze and understand their behavior, e.g., mean-field approximation, linear stability analysis, scaling, renormalization, bifurcation, chaos, pattern formation, and phase transition. Python will be used as a primary computer programming language for modeling and simulation. Prior computer programming experience is helpful, but not strictly required. Prerequisites: Graduate standing and basic knowledge of calculus, linear algebra and probability theory, or permission of instructor. Crosslisted with ISE 423. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

Sample electives

• SSIE 553 - Operations Research

  Operations research (OR) is devoted to the determination of the best course of action of a decision problem, given resource restrictions. Course provides the engineer with a firm grounding in the use of OR (mathematical) techniques devoted to the modeling and analysis of decision problems. Techniques include the following: decision modeling; linear, integer and dynamic programming; emerging optimization techniques (e.g., genetic algorithms, simulated annealing, etc.); game theory; and queueing theory. Problem areas include the following: transportation models; project/production scheduling; inventory models; assignment problems. Prerequisite: Graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 605 - Appl'd Multivar. Data Analysis

  Course introduces different multivariate data analysis and modeling tools, which can be used for simultaneously analyzing data with multiple dependent variables. It is designed to emphasize applied methodologies and applications in multivariate data analysis, especially in engineering fields. Topics to be covered include: multivariate regression, logistic regression, multivariate analysis of variance (MANOVA), principal components analysis, cluster analysis, canonical correlation, factor analysis, and discriminant analysis. The effective use of advanced data analysis software, such as SAS, for solving real-world engineering problems will also be addressed. Prerequisite: SSIE 505 or its equivalent. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 631 - Foundations Of Neural Networks

  Covers theory and practical applications of artificial neural networks. Neural networks are a broad class of computing mechanisms with active research in many disciplines, including all types of engineering, physics, psychology, biology, mathematics, business, medicine and computer science. Emphasizes the practical use of neural networks for industrial problems such as pattern recognition, predictive models, pattern classification, optimization and clustering. Topics include learning rules, paradigms and validation. Prerequisites: SSIE 505 or equivalent and SSIE 520, and knowledge of at least one programming language. Term offered varies. 3 credits. Levels: Graduate

• SSIE 641 - Adv Topics in Network Science

  This course provides concepts, models, methods and tools developed in the rapidly advancing field of Network Science. Instructions will be largely based on primary literature published recently. Topics to be discussed will include: Complex network topologies, methods for network analysis, visualization and simulation, models of dynamical/adaptive networks, techniques for mathematical analysis, network stability and robustness, and applications to social, biological and engineering systems. Prerequisites: SSIE 523 or permission of the instructor. Students taking this course should have solid knowledge of linear algebra, probability and statistics, and differential equations. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

Thesis option:

4 electives (at least one at 600-level) plus 6 credits of thesis work followed by oral presentation and defense.

Project option:

5 electives (at least one at 600-level) plus a project of at least 3 credits.

Coursework-only option:

6 electives, including at least one of the following 600-level courses that include project-based coursework to serve as capstone for the termination requirement:
SSIE 605, SSIE 612, SSIE 613, SSIE 615, SSIE 616, SSIE 617, SSIE 621, SSIE 631, SSIE 633, SSIE 637, SSIE 641, SSIE 643, SSIE 644, SSIE 650, SSIE 660, SSIE 661, SSIE 664, SSIE 665, SSIE 671, SSIE 673.

PhD in Systems Science (PhD SS)

Degree requirements include:

• satisfaction of the learning contract, including proficiency in teaching and residence requirements
• pass a comprehensive exam
• presentation of a colloquium on proposed research
• acceptance of a prospectus outlining dissertation research
• submission of a dissertation, and
• defense of a dissertation at oral examination

Application Deadline

Admission to the program occurs on a rolling basis.