Whether you study oncampus, online or in Manhattan, the Systems Science graduate program at Binghamton University offers
a unique, internationally recognized, transdisciplinary learning and research experience.
In our program, you'll learn the concepts, principles and methods for understanding,
modeling, analyzing, optimizing and improving various forms of complex systems.
Programs Offered
 PhD in Systems Science
 MS in Systems Science
 MS in Systems Science with Health Systems concentration
 MS in Systems Science: Executive Health Systems concentration in Manhattan
 Advanced Graduate Certificate in Complex Systems Science and Engineering (available
for all majors)
Selected Research Topics
 Mathematical Modeling of Systems
 Computer Simulation
 Soft Computing
 Machine Learning
 Intelligent Control
 Optimization
 Statistical Modeling
 Data Analytics
 Information Theory
 System Dynamics
 Nonlinear Dynamics
 AgentBased Modeling
 Game Theory
 Computational Social Science
 Complex Networks
 Decision Making and Management
 Health Systems; Sustainability
 Transdisciplinary Research
Required Course Descriptions, Electives
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 SSIE520 or SSIE523.

SSIE 500  Computational Tools
This course will introduce students to several programming languages and basic
programming techniques, with the focus on developing practical codewriting 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 objectoriented 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 realworld data. 3 cred
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. Crosslisted
with ISE 440. Offered in the Fall semester. 3 cred
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; studentt, x squared
and F distributions; hypothesis testing; contingency tables, goodness of fit, nonparametric
statistics, regression and correlation. Prerequisite: one year of calculus. Offered
in the Fall semester. 3 cred
Levels: Graduate, Undergraduate

SSIE 520  Modeling And Simulation
Stochastic processes, review of probability and statistics, covariance, input data
selection, random number generators, nonparametric tests for randomness, generation
of random variates, output data analysis, terminating and nonterminating simulations,
model validation, comparison of alternatives, variance reduction techniques, sensitivity
analysis, experimental design and predictive models. Prerequisite: SSIE 505 or equivalent.
Offered in the Spring semester. 3 cred
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 lowerlevel components interacting with each other in a nonlinear way. Discusses
several computational modeling frameworks, including agentbased models (particle
models, ecological and evolutionary models, gametheoretic models), complex network
models (smallworld and scalefree 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., meanfield
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 consent of instructor.
Offered in the Fall semester. 3 cred
Levels: Graduate, Undergraduate
Sample electives

SSIE 519  Applied Soft Computing
Covers relatively new approaches to machine intelligence known collectively as
soft computing. Introduces various types of fuzzy inference systems, neural networks
and genetic algorithms, along with several synergistic approaches for combining them
as hybrid intelligent systems. Emphasis is on applications, including modeling, prediction,
design, control, databases and data mining. The undergraduate students are not required
to do projects on the same level as the graduate students, and are not required to
place the degree of emphasis on hybrids. Prerequisites: basic knowledge of calculus
and discrete mathematics, and competence in at least one programming language, or
consent of the instructor. Crosslisted with ISE 419. Offered in the Fall semester.
3 cred
Levels: Graduate, Undergraduate

SSIE 617  Fuz. Sets, Fuz.Logic &
Fuz.Sys
Course consists of two parts. The first part covers fundamentals of fuzzy set theory
and the associated fuzzy logic. The second part is devoted to applications of the
theory. Topics of the theoretical part include basic concepts of fuzzy set theory
and fuzzy logic; representations of fuzzy sets; extension principle that facilitates
fuzzifications of classical mathematical concepts; aggregation operations on fuzzy
sets; the concept of a fuzzy number and arithmetic operations on fuzzy numbers; fuzzy
relations; fuzzy relation equations; basic ideas of fuzzy logic; possibility theory
based on fuzzy sets; and information aspects of fuzzy sets. In the application part,
methods of constructing fuzzy sets in various application contexts are overviewed
and representative applications of fuzzy sets and fuzzy logic are examined. The application
areas covered include systems science; approximate reasoning in expert systems; database
and information retrieval systems; pattern recognition and image processing; decision
making; medicine; economics; psychology; and various areas of engineering. Prerequisites:
SSIE 505 or equivalent and calculus and discrete mathematics, or consent of instructor.
Offered in the Spring semester. 3 cred
Levels: Graduate

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. Term offered varies. 3 cred
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.
Fall or Spring. 3 credits.
Levels: Graduate, Undergraduate
Thesis option:
4 electives (at least one at 600level) plus 6 credits of thesis work followed by
oral presentation and defense.
Nonthesis option:
5 electives (at least one at 600level) plus a project of at least 3 credits.
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.