Course Descriptions

Government, academia and industry have spent a great deal of time, effort, and money dealing with increases in the volume, variety, and velocity of collected data. Collection methods, storage facilities, search capabilities, and analytical tools have all needed to adapt to the masses of data now available. Google paved the way for a new paradigm in Big Data, with two seminal white papers describing the Google File System, a distributed file system for massive storage, and MapReduce, a distributed programing framework designed to work on data stored in the distributed file system. This course introduces the student to the concepts of Big Data and describes the usage of distributed file systems and MapReduce programming framework to provide skills applicable to developers and the data scientist in any facet of industry.

Modern computer information systems are ever-increasing in complexity and sophistication. As a result, software engineers must be able to make effective deciions regarding the strategic selection, specification, design, and deployment of information systems. Therefore, this course addresses the topics of architectural design that can significantly improve the performance of computer information systems. The course introduces key architectural concepts, techniques, and guidance to software engineers to enable them to make more effective architectural decisions.

This course provides guidelines for CSC data security utilizing cloud computing by determining the boundaries of the cloud service provider (CSP) responsible for ensuring that customer data is properly secured. Depending on the cloud services (i.e. IaaS, PaaS, SaaS), the security of the data is the responsibility of the CSC themselves. For example, in some cases the CSP may be responsible for restricting access to the data, while the CSC remains responsible for deciding which cloud service users (CSUs) should have access to it, and the behavior of any scripts or applications with which the CSU processes the data. This course identifies the security controls protecting CSC data that can be used in the different stages of the full data lifecycle. These security controls can differ when the security level of the CSC data changes.

Description:This course addresses what agile methods are, how they are implemented, and their impact of software engineering. A variety of agile methods are described, including but not limited to: Scrum, Extreme Programming, and Crystal Clear. The concerns associated with planning and controlling agile projects, along with the implications fo agile development on the customer-developer dynamic are analyzed.

Description:Recent years have witnessed the advent of wireless mobile and sensor technologies and the proliferation of application scenarios whereby large numbers of pervasive computing devices are connected to a wireless networking infrastructure in an ad hoc manner. The student is shown how to design, implement, and deploy location/context-aware applications that interact with Service Oriented Architecture (SOA) solutions. Topics to be covered include: basic user interfaces, application design, concurrency, and location-aware and other context-aware progamming.

Description:This course focuses on the design principles for secure computer systems. Topics regarding authentication, access control and authorization, discretionary and mandatory security policies, secure kernel design, secure operating systems, and secure databases are covered from a systems architecture perspective. Emphasis is on the design of security measures for critical information infrastructures. Upon completion of this course, the student is able to design, implement, and manage secure computer systems through the design of a security awareness program.

This course focuses on topics related to the design and implementation of secure data stores. Emphasis will be placed on multi-level security in database systems, covert channels, and security measures for relational and object-oriented database systems. This course teaches how to recognize the insecurities present within common database systems and how these flaws can leave a database wide open to attack. The course covers how hackers discover and exploit vulnerabilities to gain access to a data store.

Description:This course covers fundamental concepts, principles, and practical networking and inter-networking topics relevant to the design, analysis, and implementation of enterprise-level trusted networked information systems. Topics include networking and security architectures, techniques, and protocols at the various layers of the internet model. Security problems in distributed application environments are analyzed and solutions discussed and implemented.

This course will serve as a catalog of commonly used design patterns, prominent and dominant software patterns, and their applications. This course is divided into three modules. First, Software Architecture Patterns covers the various architectural patterns of object-oriented, component-based, client server, and cloud architecture. The need for software patterns is described. The various architectural patterns are listed and explained in detail in order to convey the what, where, why and how of architectural patterns. Second, Enterprise Integration Patterns covers enterprise application integration patterns and how they are designed. Patterns of service-oriented architecture (SOA), event driven architecture (EDA), resource-oriented architecture (ROA), big data analysis architecture, and microservice architecture (MSA) will be carefully studied. Finally, Patterns for Containerized and Highly Reliable Applications covers advanced topics such as Docker containers, high-performance, and reliable application architectures. Key takeaways include understanding what architectures are, why they are used, and how and where architecture design and integration patterns are being leveraged to build bigger and better systems. Cross-listed with NGEN 585.

This project course serves as a capstone for the specialization in Information Security. The class focuses on techniques for protecting critical information infrastructures through case studies, application development, and systems assessment, while the project’s activities encompass research, development and analysis/synthesis for a particular problem or opportunity.

This course explores a collection of topics in Software Architecture and Microservices and introduces concepts and best practices of software architecture. It deals with; high-level building blocks that represent the underlying software system, how a software system is structured, and how that system’s elements are meant to interact. Fundamentals of software architecture, its principles, elements, components, configurations and architectural structures and styles will also be discussed. Special focus will be given to the interaction between quality attributes and software architecture. Societal and ethical implications of software architecture and microservices will also be discussed.

This course will introduce various software testing techniques such as; unit testing, integration testing, system testing, acceptance testing, and regression testing, types of software errors, reporting and analyzing software errors, problem tracking systems, test planning, test case design, and verification & validation. The course also explores functional (black box) methods for testing software systems, reporting problems effectively and planning testing projects. Students will apply testing techniques that they have learned, throughout the course, to a sample application.

This course explores a collection of topics in Software Testing Principles and Techniques. It introduces testing techniques, software quality fundamentals, and focuses on software quality assurance for the entire software development lifecycle. It covers topics such as; Quality factors, Software Quality Requirements, Reviews, Software Audits, Software Configuration Management, Policies, Processes, and Procedures, Measurement, Risk Management, Software Quality Assurance Plan, Software Quality Models, Test Automation, Testing Tools, Black Box and White Box testing techniques. The Pareto Principle Applied to Software Quality Assurance, and Software Testing Strategies will also be discussed.

This course will serve as a catalog of commonly used open source software in the design and implementation of software solutions. The student will be exposed to open source project structure, work on an open source project, and be expected to make a significant contribution through their own custom design projects.

This course provides an overview of scientific computing and covers: Solution of Linear Algebraic Equations, Interpolation and Extrapolation, Integration and Evaluation of Functions, Random Numbers, and Sorting. The course uses C++ programming language as the base language to solve the problem sets. The student may choose to use another programming language as well. The course is conceived as an introduction to the thriving field of numerical simulation for computer scientists, mathematicians, engineers, or natural scientists without an already strong background in numerical methods.

Scientific Computing II covers: root finding and nonlinear sets of equations, minimization or maximization of functions, eigensystems, fast Fourier transform, Fourier and spectral applications, statistical description of data, and modeling of data. The course uses C++ programming language as a base language to solve the problem sets, or a student can choose another programming language. The course is intensely practical with fully worked examples and graded exercises.

Description:This course contains abstract models of computation and computability theory including formal languages, finite automata, regular expressions, context-free grammars, pushdown automata, Turing machines, primitive recursive and recursive functions, and decidability and un-decidability of computational problems.

Description:This course is a first-year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implantation. This class overs techniques used to analyze problems and algorithms (including asymptotic, upper/lower bounds, best/average/worst case analysis, amortized analysis, complexity), basic techniques used to design algorithms (including divide and conquer/greedy/dynamic programming/heuristics, choosing appropriate data structures), and important classical algorithms (including sorting, string, matrix, and graph algorithms), and data structures.

This course introduces the principles and implementations of operating systems and networking. The operating system manages hardware resources and provides a simplified interface for programs to use these resources. Networking allows different computers to communicate and potentially act as a larger virtual system. These topics are closely related; networking is often managed by the operating system (and always requires use of the hardware it manages) and the operating system uses the network to provide services like the file system. C++ language is needed to facilitate out study to these topics which provides low-level access to the hardware and is often used in operating systems and networking.

This course is an integration process of key notions from algorithms, computer architecture, operating systems, compilers, and software engineering into one unified framework. This is done constructively, by building a general-purpose computer system from the ground up. In the process, many ideas and techniques are used in the design of modern hardware and software systems, and discuss major trade-offs and future trends. This is a hands-on course, evolving around building the full set of HW and SW modules including the chip set of simple computer using a simulator, developing the assembler, building part of the virtual machine translator and a simple compiler all the way to a simple programming language and a simple game.

This course is about the use of simulation to make better business decisions in application domains from healthcare to mining, heavy manufacturing to supply chains, and everything in between. It is written to help both technical and non-technical users better understand the concepts and usefulness of simulation. The student can use the programming languages of their choice or use an off-the-shelf software to implement their projects.

Description:This course introduces the basic idea of machine learning and the application to data from real world problems. Topics include: Classification as a Problem Solving Tool, Similarity Measures and Clustering, The Classification Process, Classification for Sentiment Analysis, Advanced Recommendations, FFT Classifiers, Computer Vision & Pattern Recognition, Dimensionality Reduction, and Big Data & Machine Learning.

Statistical pattern recognition techniques are used to design automated systems that improve their own performance through experience. This course covers the methodologies, technologies, and algorithms of statistical pattern recognition from a variety of perspectives. The objective is to provide a reasonable answer for all possible data and to classify input data in to objects or classes based on certain features. After taking the course, the student should have: a clear understanding of the design and construction and a pattern recognition system; major approaches in statistical and syntactic pattern recognition; some exposure to the theoretical issues involved in pattern recognition system design such as the curse of dimensionality and clear working knowledge of implementing pattern recognition techniques.

This course focuses on explaining and demonstrating the limitations and tradeoffs of various digital image representations, such as computed 3-D images, grayscale versus color, and tools such as wavelet transforms and image compression techniques. Additionally, displaying the ability to manipulate both binary and grayscale digital images using morphological filters and operators to achieve a desired result; showing how higher-level image concepts such as edge detection, segmentation, representation, and object recognition can be implemented and used.

The course introduces the student to the interdisciplinary field of cybersecurity. Topics include the evolution of information security into cybersecurity and exploring the relationship of cybersecurity to organizations and society. The analyses of the threats and risks to/in these environments are examined. The ultimate goal of this course is for the student to acquire the advanced knowledge required to develop the skills needed to integrate knowledge from this course into a workplace environment.

Description:This course integrates cyber risk management into day-to-day operations. Additionally, it enables an enterprise to be prepared to respond to the inevitable cyber incident, restore normal operations and ensure that the enterprise assets and the enterprise’s reputation are protected. This course focuses the student on a broad range of topics relative to risk-based planning for enterprise cybersecurity. The intent is to focus on creating risk assessment and modeling approaches to solve cybersecurity issues so organizations can build security framework and sustain a healthy security posture. This course analyzes external and internal security threats, failed systems development and system processes and explores their respective risk mitigation solutions through policies, best practices, operational procedures, and government regulations.

This course integrates knowledge accumulated from the prerequisites and serves as a capstone for the concentration in Computer Security. Attention is focused on the techniques for protecting critical information infrastructures and the process of identifying the risk to data and information using case studies, application development, and systems assessment.

Digital Forensics is “the application of computer science and investigative procedures for a legal purpose involving the analysis of digital evidence.” Digital forensics encompasses much more than just laptop and desktop computers. Mobile devices, networks, and “cloud” systems are very much within the scope of the discipline. It also includes the analysis of images, videos, and audio (in both analog and digital format). The goal is to provide digital evidence that are obtained (both in direct and indirect ways) from digital media. The course focuses on the analysis of authenticity, comparison, and enhancement as the main vehicle to obtain digital evidences (both in direct and indirect ways) from digital media.

Biometric security systems is a rapidly evolving field with applications ranging from accessing one’s computer to gaining entry into a country. Biometric systems rely on the use of physical or behavioral traits, such as fingerprints, face, voice, and hand geometry, to establish the identity of an individual. The deployment of large-scale biometric security systems in both commercial and government applications increases the public’s awareness of this technology. This rapid growth also highlights the challenges associated with designing and deploying such systems. The core computational component of biometric systems is biometric identification (or recognition), and it is indeed a grand challenge in its own right. The purpose of this course is to expose the student to current biometric identification techniques and systems, teach them to coin their own biometric security applications through capturing human biometric traits, creating unique identifications for them, build classification systems that can identify individuals, and make decisions to maintain security parameters.

Description:This course explores a topic or collection of topics of special interest that is timely and in response to critical or emerging topics in the broad field of computer information sciences.

This course explores a topic or collection of topics of special interest that is timely and in response to critical or emerging topics in the broad field of scientific computing in computer information sciences.

This course explores a topic or collection of topics of special interest that is timely and in response to critical or emerging topics in the broad field of software engineering and software testing in computer information sciences.

This course explores a topic or collection of topics of special interest that is timely and in response to critical or emerging topics in the broad field of cybersecurity in computer information sciences.

This course explores a topic or collection of current topics that are timely and in response to critical or emerging topics in the broad field of computer information sciences.

This course explores a topic or collection of current topics that are timely and in response to critical or emerging topics in the broad field of scientific computing computer information sciences.

This course explores a topic or collection of current topics that are timely and in response to critical or emerging topics in the broad field of software engineering and software testing in computer information sciences.

This course explores a topic or collection of current topics that are timely and in response to critical or emerging topics in the broad field of cybersecurity in computer information sciences.

This course allows the student to pursue an area of interest that is within the broad scope of Computer Information Sciences. A faculty member will supervise this study.

This course attempts to change the way students learn and think about the design, organization and hardware of a computing system architecture to meet goals and functional requirements of future technological developments, demystify computer architecture through an emphasis on cost-performance-energy trades-offs and good engineering design. This will help the student to build rigorous quantitative foundation of long-established scientific and engineering disciplines. A special emphasis will be put on demonstrating these concepts through the “Putting It All Together” approach at the end of the set of necessary modules. Modules include pipeline organizations and memory hierarchies of the ARM Cortex A8 processor, the Intel core i7 processor, the NVIDIA GTX-280 and GTX-480 GPUs, and one of the Google warehouse-scale computers, to apply the cost-performance-energy principles to this material, and memory is critical resource for the rest of the modules.

This course discusses and advocates a structured approach to parallel programming. This approach is based on a core set of common and composable patterns of parallel computation and data management with an emphasis on determinism and scalability. By using these patterns and also paying attention to a small number of factors in algorithm design (such as data locality), programs built using this approach have the potential to perform and scale well on a variety of different parallel computer architectures. A special emphasis will be put on both collective “data-parallel” patterns as well as structured “task-parallel” patterns such as pipelining and superscalar task graphs. The structured patter-based approach, like data-parallel models, addresses issues of both data access and parallel task distribution in a common framework. Optimization of data access is important for both many-core processors with shared memory systems and accelerators with their own memories not directly attached to the host processor. Extensive use of pertinent and practical examples from scientific computing will be made throughout. The programming languages used will be Python, Fortran, or C++. Both the shared and distributed paradigms of parallel computing will be covered via the OpenMP and MPI libraries.

Real-world problems entail a hierarchy of systems that interact in complex ways. This causes such complex problems not to lend themselves to easy solutions with computational methods like classical parametric machine learning. The complexity arises from three main causes: high-dimensionality, unknown function properties, and computationally expensive analysis and simulation. These challenges with the presence high volume/velocity streaming data severely aggravate the difficulty and become the bottleneck for any computational solution. This course helps the student to explore some advanced modeling and optimization methods that can help solve such problems. Deep Learning (DL) allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. DL has the ability to discover convoluted structure in large data sets by using say the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. A special emphasis will be put on how to build applications using this approach that have the potential to perform and scale well on a variety of different previously studied parallel computing systems. Extensive use of parallel programming models like CUDA, C, Python, OpenMP and may be Fortran will be to conduct weekly projects.

This course is about leading the student to explore some heavy research on a certain high-dimensional problem under the supervision of a research scientist in one of the computational sciences subdomains. The course outcome is expected to be the foundational part of a published research paper to be presented (later after augmented with other research work) in a research symposium. Special emphases put on how to build programs using this approach that have the potential to perform and scale well on a variety of different previously studied parallel computing systems. Extensive use of parallel programming models like CUDA, C, Python, OpenMP and may be Fortran will be to conduct weekly projects.

This is the second of the depth-level research explorations courses. The goal of this course is to continuous the realization efforts from course work of CISC 727. A published research paper on a computational solution in deep learning for the real-world problem selected in the prerequisite course is the expected outcome for this course. The paper is to be presented later after augmented with other research work in a research symposium. This paper should be a step toward choosing the research topic for the doctoral dissertation for the degree.

The course is of two parts: one, to allow the student to make progress on their research in a structured way and to help fulfill program requirements, and two, to present professionalization information crucial to success in the field. The course is organized largely around working on the research paper, with the goal of making it a conference-presentable and journal-publishable work.

This course is the first of the two Doctoral Research Seminar courses. The course provides the student with the theoretical background and practical application of various research methods that can be used in computational sciences. The course provides a look to the research process and literature review and study the correlation and experimental research methods and design. Students will analyze several existing research studies and design and conduct studies. The principal work in this course is the research and writing of a substantial paper in a field related to the Ph.D. dissertation of each student. The student is expected to have a research topic and primary source base identified and the topic approved by the dissertation adviser.

This course is the second of the two Doctoral Research Seminar courses. The course provides a deeper look to the research process, implementation methodology and research findings. The student will analyze several existing research studies and design and conduct studies. This course emphasizes advanced research goals and mastery of the relevant sub field. approved by the dissertation adviser.

This is an individual study course for the doctoral student that culminates in a Ph.D. Thesis. Content to be determined by the student and the student’s Doctoral Committee. The Computational Sciences thesis is an implementation of a serious experimental research that involves the formulation of a deductive model that makes novel and unforeseen predictions which should be then tested objectively and confirmed under conditions unfavorable to the hypothesis. In addition to a well written thesis, the student is required to deliver the computational solution in a specific domain. In support of their findings, the student is required to introduce a software package that meets the criteria of excellent software requirement. The thesis needs to show that the writer can produce their extended piece of work, in perfect English, and respects the standards of form and structure. May be repeated for credit.

This course builds on the skills acquired in ENGL 105 or ENGL 106. The student continues to study the process of effective communication, based on an understanding of purpose and audience using speaking techniques such as enunciation and modulation. The student builds an understanding of the basic skills needed to communicate across disciplines.

This survey course investigates major movies, historical events, schools of thought, and developments in the history of film and mass communication. Through readings, lectures, and independent research, the student focuses on the relationships between history, technology, and media development and explores the impact motion media and mass communication have on society and the economy.

Programming fosters creativity, reasoning, and problem solving. The programmer gets the opportuinty to create something from nothing, use logic to turn programming contructs into a form that a computer can run, and, when things don’t work quite as well as planned, use problem solving to figure out what has gone wrong. Programming is fun and the skills learned from it can be useful both in school and at work, even if your career has nothing to do with computers. The non-credit camp will introduce you to the most widely commercial programing language used for gaming, C++. In this week long camp we will go over the basics of the language, giving you a good foundational understanding of the programming language syntax, the machine it’s running on, and how to effectively create an application. We will walk step by step through the concepts and skills needed to start yourself on the path to becoming an independent game developer. There are also programming puzzles at the end of most chapters for you to try, which will help improve your programming skills.

This course introduces the concepts and techniques of computer programming with Java. Emphasis is placed on developing the student’s ability to apply problem-solving strategies to design and implement algorithms in a modern, Object-Oriented Programming language.

This course further develops the concepts and techniques of computer programming. Emphasis is placed on structured programming, top-down design, more advanced data structures, and the proper use of the Java programming language development tools.

This course introduces the student to developing J2EE Web Applications in Java using Oracle’s JDeveloper IDE. This course provides and in-depth exploration of Object-Orinted concepts and techniques of Java programming in the JDeveloper environment. Emphasis is placed on devloping web applications with Servlets and Java Server Pages (JSPs) in J2EE.

This course provides students with basic concepts of cybersecurity fundamentals. Students receive a basic awareness of threats to information systems, vulnerabilities inherent to modern architectures and the options available to mitigate threats within a system.

This course provides students with an understanding of information security policy and how to apply industry and government best practice rules and guidelines. Students begin to create, implement, and institutionalize organization policies that ensure legal, ethical, privacy and compliance standards which are supported. The course compares and contrasts the role of government versus commercial security, to include funding, culture, stakeholders, and classified information protections.

Relating to people, process and technology this course provides students the methods to think through and solve complex global cybersecurity problems. Students are introduced to specific methodologies regarding systems engineering and processes, such as Shewhart cycle, OODA loop, and RPR diagnosis as they apply to networks and cybersecurity.

This course provides students with a basic awareness of the options available to understand and mitigate threats within a system. It demonstrates the methodologies used by threat actors to exploit systems and how cybersecurity professionals can architect more secure systems that support business imperatives, while mitigating cyber risks. An overview of applications, networks, mobile devices, wireless security, and tool categories such as firewall, SIEM, and vulnerability management tools are discussed.

This course provides students with a basic awareness for building defense in depth architectures and security controls into an organization’s technology processes, workforce considerations and network architectures. As part of this course, students gain a preliminary understanding of cryptography and concepts such as authentication.

This course provides students with foundational knowledge in secure networking concepts, technology, and administration. Network security is an essential requirement in building a secure resilient infrastructure. Students learn technical and administrative components of secure networks, how to apply secure design principles to configure network resources (routers, switches, firewalls, IPSes) and how to securely administer the network. Threats to network security are examined and security controls are designed to appropriately mitigate risks. Network and system administration policies and procedures are examined.

This course provides students with the ability to develop policies, plans and processes necessary to implement and measure an effective, risk-based cybersecurity program. Students synthesize current delivery practices and methodologies for measurable results within the cybersecurity program. Students in the management and leadership concentration complete this course.