Whether you study on-campus or in Manhattan, the Systems Science graduate courses at Binghamton University offer unique, internationally
recognized, transdisciplinary learning and research experiences. 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

Agent-Based 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 SSIE-520 or SSIE-523.

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. 3 cred
Levels: Graduate, Undergraduate

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. Cross-listed
with ISE 440. Offered in the Fall semester. 3 cred
Levels: Graduate, Undergraduate

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. Offered
in the Fall semester. 3 cred
Levels: Graduate, Undergraduate

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.
Offered in the Spring semester. 3 cred
Levels: Graduate, Undergraduate

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 consent of instructor.
3 credits
Levels: Graduate, Undergraduate

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. Cross-listed with ISE 419. Offered in the Fall semester.
3 cred
Levels: Graduate, Undergraduate

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.
3 credits
Levels: Graduate

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

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 600-level) plus 6 credits of thesis work followed by
oral presentation and defense.

Non-thesis option:

5 electives (at least one at 600-level) 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.