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 counterintuitive
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 黑料视频, 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.
Degrees offered
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Watson College offers its MS and PhD in Systems Science in person and online via the
EngiNet distance learning service.
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PhD in Systems Science (on-campus and online)
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MS in Systems Science (on-campus and online)
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MPA/MS in Systems Science double degree
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MS in Genocide and Mass Atrocity Prevention/MS in System Science double degree
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Advanced Graduate Certificate in Complex Systems and Engineering
- Combined 4+1 BS ISE-MS Systems Science Program for Binghamton undergraduates
Join us at an upcoming , in person or online, to learn more about our Systems Science PhD, MS and specialized
concentrations.
Research topics
Areas of specialization are multidisciplinary and reflect the talents and interests
of Watson College faculty members. Available research topics include:
- Agent-Based Modeling
- Artificial Life
- Complex Networks
- Complex Systems
- Computational Social Science
- Computer Simulation
- Data Analytics
- Decision Making and Management
- Game Theory
- Health Systems
- Information Theory
- Intelligent Control
- Machine Learning
- Mathematical & Computational Biology
- Mathematical Modeling of Systems
- Nonlinear Dynamics
- Optimization
- Soft Computing
- Statistical Modeling
- Sustainability
- System Dynamics
- Transdisciplinary Research
Master of Science in Systems Science curriculum
The Master of Science in Systems Science provides the student with concepts, principles
and methods for understanding, modeling, analyzing, optimizing and improving various
forms of complex systems. Such systems thinking and problem- solving skills will be
an invaluable asset when the student blazes a new trail in any scientific, engineering,
business or management profession in today鈥檚 increasingly complex world. This program
is available fully online.
Required courses
Students must complete four required courses while maintaining at least a B average.
Students will choose to complete either SSIE-520 or SSIE-523.
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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
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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
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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
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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
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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
Degree-completion options
- Thesis: Four additional graduate-level elective courses (at least one at 600- level), plus
6 credits of thesis work followed by oral presentation and defense.
- Project: Five additional graduate-level elective courses (at least one at 600-level), plus
a project of at least 3 credits followed by oral presentation and defense.
- Coursework only: Six additional graduate-level elective courses, including at least one 600-level course
that contains project-based coursework to serve as capstone for the termination requirement.
Sample electives
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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
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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
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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
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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
View current course offerings.
PhD in Systems Science
The doctoral program in systems science offers a unique, internationally recognized,
highly transdisciplinary learning and research experience that is available in person
or fully online.
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