Courses Available in the ECE Graduate Programs

ECE Graduate Level Courses

ECE 09.504: Special Topics in Electrical and Computer Engineering

This course covers timely topics in electrical and computer engineering related to engineering practice and/or research. It is in fact a series of courses that constitute the emerging topics in electrical and computer engineering sequence. These classes can be taken multiple times when approved by the advisor. Multiple sections of this course are offered during each semester with different content on emerging topics. The following courses have recently been taught in this class: Computer Networks; Electronic Packaging; Nanotechnology; DSP Architectures; RF Design; Deep Learning; Microwave systems; Smart Satellites and Internet of Things. 

ECE 09.509: Virtual Reality Systems

Virtual Reality (VR) Systems covers the architecture and design of current generation systems for creating 3D VR environments. Topics included are application/hardware architecture, pipeline development, geometric transformations in a 3D coordinate system, geometry and pixel shading, lighting systems, texturing and VR development. Students will be exposed to current VR technologies and next generation algorithms. As a graduate level course, students are expected to gain a solid foundation in GLSL shader theory, advanced object oriented design techniques, pathfinding algorithms, and apply these techniques to independent research experience to a problem of their choosing.

ECE 09.510: Advanced Alternate Energy Systems

This course introduces the basics and current trends of the electric power system and electric power industry. Students will learn methods to mathematically analyze different renewable electric energy systems and evaluate their performance, economics, and sustainability. Specifically, key basics of wind and solar energy technologies, and their power grid integration issues will be extensively discussed. Opensource software will also be introduced to the class to assist their study, such as PVWatt from NREL. Other alternate energy sources, such as CHP, Microturbine, biomass, PHEV, Microgrid, etc. will also be introduced. After finishing this course, students are expected to be able to conduct a critical analysis of national and global energy systems. The advanced graduate level version of this course features a research project and presentation component for graduate students to identify, research and present renewable energy integration technologies into modern power systems that are relevant to this course.

ECE 09.515: Emerging Electricity Markets

This course will provide ECE graduate students with a basic understanding of the power system restructuring, market design, and pricing mechanisms in different physical and financial electricity markets. Rigorous mathematical formulation and MATLAB based simulation will provide students with an in-depth understanding on the major differences between the conventional regulated and the emerging restructured power system operation paradigms, and adequate training to solve the different system operation problems. This course will feature a research project and presentation component for graduate students in exploring current industry practices and challenges in electricity market operations.

ECE 09517: Technologies Towards Green Energy Future (coming soon)

There is a transformational change in the electric power systems and electric power industry fueled by renewable “green” energy sources. This introductory course will provide an overview of the fundamental technologies that enable this transformation, starting with a review of the current energy sources for electricity generation, including natural gas, nuclear, hydro, wind, solar, coal, and others. Continuously changing positions of these sources in the electric energy portfolios will be discussed to illustrate the increased importance of renewable sources. There are, of course, significant challenges that renewable energy faces, such as low efficiency/high-cost harvesting, uncertainty/intermittency of power output, as well as system integration issues and their impacts. These challenges will be addressed via solutions using advanced controllers, smart inverters, and energy storage technologies. Along the way, several key and fundamental concepts will be introduced, such as power and energy calculation, instantaneous/average/active/reactive power, three-phase systems, power factor, phasors, network equations, power quality, induction and synchronous machines, which will prepare students to subsequent courses in this sequence. This course will also include hands-on design projects for harvesting renewable energy. The Graduate level course will specifically emphasize an independent course project.

ECE 09518: Wind Energy System Planning and Operation (coming soon)

Wind energy is one of the most critical green renewable energy sources in the U.S., with significant – and still yet unrealized – potential for wind power capacity and generation across the nation. Realizing that potential requires financially viable and physically feasible long-term planning, short-term operation, and real-time control of land-based and offshore wind farms, which constitute the primary focus of this course. Specifically, this course will introduce HVAC (High Voltage Alternating Current) and HVDC (High Voltage Direct Current) transmission technologies and compare them to integrating wind energy projects into the power grid. Specific topics discussed in detail include generation system reliability and cost analysis, coordinated generation system and transmission system planning, planning under changing market environments, economic dispatch of wind energy systems, and optimal power flow with wind energy integration. Industrial power system planning and operation tools aided by a set of hands-on labs that use industry-standard simulation systems such as MATLAB/Simulink and Opal-RT will also be introduced. This Graduate level course will specifically emphasize an independent course project.

ECE 09.521: Fundamentals in Systems Engineering

Systems Engineering is the interdisciplinary approach and means to enable the realization of today's complex, dynamic products and systems. Individual products such as Cell phones, aircraft, automobiles, computers and even household appliances are made up of parts developed by many people with varied skill sets, often working for different companies and from remote locations. Other systems such as transportation, energy generation and distribution, medical, communications, emergency response and similar are very complex as they are composed of many varieties of products and systems. Systems Engineering is an integrating function that addresses all the disciplines and specialty groups resulting in a structured development process that proceeds from concept to production to operation including maintenance & support, and eventual disposal. Systems Engineering considers both the business and the technical needs, including environmental and safety, of all customers with the goal of providing a quality product that meets the user needs. It focuses on defining customer needs and required functionality early in the development cycle, documenting requirements, proceeding with design synthesis and system validation while considering the complete problem that includes - operations, cost & schedule, performance, training & support, sustainment, test, disposal, and manufacturing. The course is designed to expose the student to the system engineering process to complement their technical skill set and to cover topics that are often not covered in other classes. The course will include frequent guest lecturers who are practicing experts in the systems engineering domain. The course will utilize the latest in processes and software tools from industry such as SysML modeling and architectural documentation tools. Students will participate in a semester long project to gain hands-on experience with the course concepts. This graduate level course will also provide opportunities for team management and cultivation of leadership and communication skills.

ECE 09.523: Advanced Radar Systems^♦

This course will provide an introduction to radar systems, range equation and radar signal processing techniques as well as the nature of physical observables and propagators, the effects of the propagation medium on sensor performance, the relationship between signals and noise, and the characteristics of critical sensor functions (including detection and tracking). Radar subsystems will be studied, including antennas, transmitters, receivers, and signal processors. This will also feature a project component for students to identify, research and present open problems that are relevant to radar systems.

ECE 09.524: Advanced War Gaming and C4ISR^♦ 

This course will expose students to a comprehensive range of technologies that govern the effectiveness of our nation’s ability to effectively conduct military operations. It focuses on material drawn from a working group of distinguished thought leaders in critical technology and operations areas, thereby exposing students to the state-of-the-art thinking and philosophies. The class material will be enhanced by the study of patents that relate to the subject which were issued to the courses instructor. This course will also include advanced topics such as C4ISR algorithms, and graduate students taking this class will be expected to work on a course project involving implementation of important C4ISR algorithms.

ECE 09.525: Advanced Command and Control^♦

Command and Control (C2) is defined as the exercise of authority and direction over assigned forces in order to accomplish a mission. This course will embark on a study of C2 information processing and decision making in the context of adaptive combat systems, as well as civilian and business examples. The course topics include the following: the history of military C2, C2 decision processes (Observe-Orient-Decide-Act loops), problem sense making (Identification) and solution finding and implementation processes, operational architectures, information fusion, control theory, mission success and organizational fitness. The course will also feature a project component for which the students will identify research, execute and present a solution to a problem that is relevant to course content.

ECE 09.526: Advanced Weapon Systems^♦

This course studies system engineering principles in the weapon system components and will relate the principles used in components such as prelaunch decision processing and missile in-flight control functionality to the robustness of the overall combat system. Missile systems will be studies, including basic aerodynamics and propulsion. The engineering principles discussed will be used to develop missile guidance laws and track filters to support a robust combat system design. In addition to these, advanced topics such as track fusion and advanced guidance laws will be discussed, and graduate students taking this class will be expected to complete a project which shows competence and understanding of these advanced topics in addition to course requirements of the undergraduate version of this class.

ECE 09.527 Advanced Model Based Systems Engineering

This course is an extension of systems engineering by addressing the needs to better train and prepare students to use model-based techniques to solve complex design problems. This multi-disciplinary class is designed to use a model-based systems engineering approach to transform a set of customer needs, expectations, and constraints into a solution and to support that solution throughout its lifecycle. Students will utilize SySML, a general-purpose modeling language, for developing complex systems composed of hardware, software, information, personnel, procedures, and/or facilities. Through the use of SySML students will gain an understanding of structural, behavioral, parametric, and requirements models and their application. Students will also learn how these models can be used to inform other domain specific activities or subordinate models.

ECE 09.531 Advanced Optical Fiber Communications

This course provides a working knowledge of the components comprising fiber-optic networks and system design and analysis of fiber-optic networks. The fiber-optic networks form the backbone of today’s communication networks, including the global internet, 4G wireless, home access (e.g. FiOS), and data center networks. The operation of lasers, optical fiber, optical modulator/demodulator, receivers, and optical routers will be discussed. The applications of fiber-optics techniques, such as fiber to the home, the broadband wireless-optical interface, all-optical switching, broadband analog signal processing and computing, and security in fibers, will be discussed. The graduate version of this course will focus more on applications and experimental skills of optical fiber technologies. Students will have opportunities to access and operate high-speed optical communication system from the research lab.

ECE 09.551: Digital Signal Processing

This is a graduate level course in digital signal processing (DSP), whose applications include analysis, compression, recognition and processing of all telecommunications, audio – visual, biomedical, financial, energy demand and consumption, seismic/tectonic/oceanographic data, among many other countless systems possible. The primary goal of this class is to introduce advanced topics in signal processing and filter design approaches that allow analysis of real-world signals that cannot be easily analyzed by the basic approaches discussed in the undergraduate level DSP course. Such signals include non-stationary signals with time-varying spectra as well as those scenarios where signal and noise spectra overlap. The specific topics that will be discussed in this class include advanced filter design and implementation approaches, filter banks, analysis of random signals and spectral estimation, analysis of non-stationary signals using short-time Fourier transforms and wavelet transforms, optimal and adaptive filters, and signal separation techniques such as independent components analysis. This is an applied class and will feature several real-world projects.

ECE 09.552: Digital Image Processing

Digital image processing covers the analysis and contemporaneous applications of the enhancement, restoration, compression and recognition of monochromatic images. Both classical and state-of-the-art algorithms will be employed in conjunction with appropriate software for analyzing real-world images.

ECE 09.553: Digital Speech Processing

This course covers the fundamentals of digital speech signals and processing and simultaneously stresses real-life engineering aspects from a systems perspective. An overview of the different branches of speech processing are covered, namely, speech production, vocal tract modeling, speech coding, speech recognition, speaker recognition and speech synthesis. The building blocks of such applications, namely, linear predictive analysis and quantization (scalar and vector) are taught.

ECE 09.554: Theory and Engineering Application of Wavelets

The theory of wavelets gave rise to a substantial number of applications in many areas including various fields of engineering, making it one of the most popular research areas of all times. In this class, the theory of wavelets will be carefully developed from the ground up, with an emphasis on engineering applications. Starting with a review of Fourier based signal analysis methods, short time Fourier transform, continuous wavelet transform, discrete wavelet transform, fast wavelet algorithms, wavelet packets, wavelet networks will be discussed. Applications of wavelets such as image and audio compression, biological signal analysis, feature detection, signal denoising will also be explored.

ECE 09.555: Advanced Topics in Pattern Recognition / Machine Learning

This class will introduce a broad spectrum of pattern recognition and machine learning algorithms along with various statistical data analysis and optimization procedures that are commonly used in such algorithms. Although mathematically intensive, pattern recognition is nevertheless a very application driven field. This class will therefore cover both theoretical and practical aspects of pattern recognition. The topics discussed will include Bayes decision theory for optimum classifiers, parametric and nonparametric density estimation techniques, discriminant analysis, basic optimization techniques, introduction to basic neural network structures, and unsupervised clustering techniques. As a graduate level course, several advanced and contemporary topics will also be covered, including fuzzy inference systems, support vector machines, adaptive resonance theory, incremental learning and online learning and particle swarm optimization. Students will be expected to conduct independent research for possible publications, as part of the class project.

ECE 09.556: Advanced Embedded Software Design

Embedded systems dramatically enhance our lives and are prolific in our everyday life. It is not uncommon for Americans to come in contact with over one hundred embedded systems each day. With billions of embedded systems, being produced each year there is a huge need for engineers who can create good embedded software. This course focuses on embedded software for applications running directly on an embedded processor without an operating system. A brief survey of microcontroller technologies will be covered but the class will focus on ARM microcontrollers and the embedded peripherals available on such devices. Advanced embedded communications technologies (CAN, WiFi, Bluetooth, ZigBee, etc.) will be surveyed and at least one implemented during the course. A great emphasis will be put on good programming practices and design patterns which support working in larger groups. Additionally, students will learn project management skills and will be required to manage a team of undergraduate engineers to accomplish a real world embedded system project.

ECE 09557 - Advanced Biometric Systems

Biometrics is the science of recognizing and authenticating people using their physiological and/or behavioral characteristics. By using biometrics, it is possible to establish an identity based on “who you are”, rather than by “what you possess” (e.g., an ID card) or “what you remember” (e.g., a password). Interest in biometrics has increased significantly with a global market that is experiencing very rapid growth. Border and immigration control, restricted access to facilities and information systems, cybersecurity, crime investigations and forensic analysis are just a few of the primary application areas of biometrics used by commercial, government and law enforcement agencies. There is much research interest in different biometric systems with the main issues being high performance, ease of use and implementation, low cost and high user acceptance. This course involves the study and design of various biometric systems (fingerprints, voice, face, iris and other modalities). Multibiometric systems are also covered. This includes feature fusion, classifier fusion and systems that use two or more biometric modalities. Biometric system performance and issues related to the security, ethics and privacy aspects of these systems will also be addressed. Course principles are reinforced by a significant project or research experience.

ECE 09.558 Reinforcement Learning

Reinforcement Learning course will provide solid foundations for tackling advanced reinforcement learning problems. Students will learn about the core challenges and approaches in RL. Through a combination of lectures, projects, and written and coding assignments, students will become well versed in key ideas and techniques for RL. The course will introduce fundamental concepts of RL, including Markov Decision and Reward Processes, Dynamic Programming, Model-Free Learning, Temporal Difference, Monte Carlo search, on-policy control, off-policy methods, and policy gradient methods. Class assignments will include implementation of basic as well as advanced RL algorithms using TensorFlow topics. Besides, students will advance their understanding and the field of RL through a final project again using TensorFlow libraries. An essential component of the project will be a written report on the lines of a research paper.

ECE 09.560: Artificial Neural Networks

Artificial Neural Networks covers the design of a variety of popular neural network architectures and their contemporary engineering applications. Neural network architectures that will be studied in detail include the multilayer perceptron, radial basis function, and the Hopfield networks. State-of-the-art software will be used for network design. VLSI implementations of neural networks will be discussed.

ECE 09.566 Advanced Topics in Systems,Devices and Algorithms in Bioinformatics

Bioinformatics is the field of applying computational techniques, from mathematics, statistics, and machine learning, to the vast amounts of biological - but most specifically genomic - data. While some refer to bioinformatics only in the context of collection, storage, organization and access of such biological data within large databases, this course's view of bioinformatics will include - in fact focus on - systems and devices that generate such data, and development of methodologies and models to analyze the vast quantities of data generated by such systems and devices. The course will provide basic biological background of genomics, will introduce the students to commonly used bioinformatics databases and computational tools (such as search, alignment, and protein visualization tools) used to analyze genomic data from such databases. The focus of the course will be on basic bioinformatics systems and devices, such as high throughput next generation sequencers and gene chips, followed by an in-depth discussion on the theory of basic genomic signal processing and computational intelligence techniques used in bioinformatics, including hidden Markov models and optimization algorithms for sequence alignment and gene prediction, clustering and classification algorithms. This course will also provide students with a mechanism to conduct independent research to advance the field through development of novel algorithms and approaches

ECE 09.568: Discrete Event Systems (DES)

This course introduces fundamentals of discrete event system models and their applications in modeling, control, analysis, validation, simulation, and performance evaluation of computer systems, hardware/software co-design, manufacturing/de-manufacturing processes, communication networks, and transportation, etc. The mathematical and graphical models include graphs, finite state machine, Petri Nets, timed models, stochastic timed models, and Markov chains, etc. As a graduate level course, it also provides students with a mechanism (a) to conduct independent research on advanced and contemporary DES topics, including higher-level Petri Nets, finite automata based supervisory control, and Petri Nets in job shop scheduling, etc; and (b) to develop novel models and algorithms for DES.

ECE 09.569: System-on-Chip Verification

This course introduces students to a variety of state-of-the-art hardware design verification methods, including traditional functional simulation, assertion-based verification and a subset of formal verification techniques. Topics covered include functional simulation, coverage metrics, test bench design and automation, assertion-based verification, and property specification language (PSL). As a graduate level course, students are expected to gain a solid foundation in current, practical chip verification techniques, underlying theory, and significant independent research experience applying the techniques, particularly formal verification methods, to a real problem of their own choice.

ECE 09.571: Instrumentation

Elements of instrumentation systems are treated including transducers, signal conditioning, and signal processing. Elements of modern instrumentation systems including standards (IEEE-488, SCPI) and smart sensors are considered.

ECE 09.572: Advanced Smart Grid

The ways in which electricity is generated, transmitted, distributed, stored, and used, are the subject of revolutionary and evolutionary changes compared to the electricity grid we have today. Smart Grid goals include the improvement of grid reliability, reduction in outages, faster return on service, ability to integrate a broad range of renewable energy sources, and to include customers in the ability to effect load decisions based on grid demand and energy pricing. This course will address grid fundamentals, tools and technologies, and then address major Smart Grid subsystems including conventional and alternative generation, storage technologies, transmission and distribution systems, standards, demand management, real-time pricing, grid stability, control technologies, measurement including Smart Sensors and Advanced Metering Infrastructure. Physical and cyber vulnerabilities will also be addressed. The course will include a project to reinforce Smart Grid elements.

ECE 09.573: Advanced Smart Sensors

Elements of Smart Sensors and Smart Sensor systems are treated. Instrumentation fundamentals covered include transducers, signal conditioning, and data acquisition, communication, along with important considerations and associated standards. Relationship of smart sensors to integrated system health monitoring (ISHM) and similar Intelligent Sensor applications are addressed. The course will include a project to reinforce Smart Sensor elements and provide opportunities for research in the field.

ECE 09.582: Memristors and Nanoelectronic VLSI

This course is an advanced course in the extension of analog/digital electronic systems, dealing with CMOS devices and emerging nanoelectronic devices and technologies. Since the importance of emerging nano systems goes beyond traditional circuit theory and EE in general, this course aims to provide students with an opportunity of understanding the fundamental concepts of a set of emerging nanodevices, with particular emphases on memristors and memristive systems, and their potential applications and impacts on the next generation VLSI systems. The course will also emphasize hands-on programming and application to examples as an important means to understand and benefit from the material. Software tools such as Matlab/SPICE/Cadence will be extensively used throughout the learning and design experiments.

ECE 09.585: Advanced Engineering Cyber Security

This course addresses the need to better prepare students for the expansion in the Internet of Things (IoT) by imparting fundamental concepts and capabilities in the management of cyber security. Cyber security is key to developing large-scale, wide-area systems, which can provide the degree of security required to further implementation of highly-vulnerable, highly-visible systems such as the Smart Grid. To gain this understanding, the course addresses a number of key components: standards including network and encryption techniques (RSA, etc.) and security processes, methods of cyber attach, and some methods of software and hardware security enhancement. Course principles are reinforced by a significant project or research experience.

ECE 09.586: Advanced Portable Platform Development

The total number of Android and iOS devices is estimated to be over 4 billion devices (2020) and continues to grow. The ubiquitous nature of these devices means they are now the default choice of platforms for hardware and software developers. This course details the ARM core architectures, which underpin the majority of mobile devices, along with the basic operating system and application software environments. Principles of effective app development using available SDK tools and project management techniques are presented. Methodologies for performance analysis are treated. The hardware vs. software trade space will also be considered. The course content is reinforced with a significant development project.

ECE 09.590: Advanced Emerging Topics in Computer Engineering

This course covers special topics in emerging areas of Computer Engineering such as computer networks, mobile robotics, embedded systems and advanced architectures

ECE 09.595: Advanced Emerging Topics in Comp. Intelligence & Machine Learning

As the amount of data we generate grow astronomically, so does the need for approaches, algorithms, techniques and the hardware that can be used for effective processing, storing, and analysis of such massive volumes of data. Computational intelligence, machine learning and data mining all deal with automated analysis of large volumes of data in search of known or hidden structures, patterns and information. While well-established approaches that now form the foundations of these topics are discussed in other specifically named courses, this graduate level course will provide an advanced treatment of emerging topics - fueled by rapid growth of research and development in these areas - but that have not yet reached the mainstream textbooks. Hence, due to its very nature, the specific content of this class will be different every time it is offered, focusing on the most recent developments in these areas. Graduate students taking this class will be expected to complete a project on a class related emerging topic of their interest.

ECE 09.651: Estimation and Detection Theory

Modern estimation and detection theories can be found at the heart of many engineering systems including radar, sonar, speech, image analysis, biomedicine, communications, control and seismology. All these systems share the common problem of needing to estimate the values of a group of parameters or being able to decide when an event of interest occurs and then to determine more information about that event. In radar, we are interested in determining the position of an aircraft, as for example, in airport surveillance radar. The task of information extraction is the subject of estimation theory; and the task of decision making is the subject of detection theory. The course will showcase numerous examples that illustrate and apply the theory to current problems of interest in engineering.

ECE 09.655: Advanced Computational Intelligence and Machine Learning

Computational Intelligence and Machine Learning deal with automated classification, identification, and/ or characterizations of unknown systems based on data, typically – and increasingly – large volumes of data. This course is an advanced research-intensive graduate level course that explores the more advanced and emerging topics of computational intelligence, such as – but not limited to – graphical models, Monte Carlo approaches, incremental and online learning, learning in nonstationary environments, deep belief networks, and other topics that are emerging at the time of offering. In fact, the exact content of the course is likely to evolve over the years due to the rapid development of the field. As an advanced research intensive course, this class will involve a thorough literature search and review, followed by proposing, executing and presenting results on a novel real-world computational intelligence problem that is of research interest to the student. Students will be encouraged to publish the outcomes of their research project.

ECE 09.704 Special Topics for Doctoral Students in ECE

This class provides timely coverage of specific advanced and emerging topics in Electrical and Computer Engineering, and it is intended for doctoral students. Special topics courses may be traditional classroom-based courses as well as research-related courses supervised by specific advisers. This class may be taken multiple times when offered with a different special topics content.

General Engineering Graduate Courses Available to ECE Students 

ENGR 01.510: Finite Element Analysis

Fundamental concepts for the development of finite element analysis are introduced. The element stiffness matrices are developed using shape functions defined on the elements. Aspects of global stiffness formation, consideration of boundary conditions, and nodal load calculations are presented. Mesh division and problem modeling considerations are discussed in detail. Topics of scalar field problems and natural frequency analysis are covered. Computer applications are included.

ENGR 01.511: Engineering Optimization

The formulation and modeling aspects of engineering optimization problems are presented. These steps involve setting up of the objective function to be minimized and the resource and system constraints to be satisfied. Solution techniques using gradient based methods, zero order methods, and penalty techniques are discussed.

ENGR 01.512: Principles of Nanotechnology

This course explores the science and engineering at the nanometer scales. Topics include fundamentals of nanotechnology; types and properties of nanomaterials; methods of fabrication; how these materials are characterized and the potential applications.

ENGR 01.569 Connected Vehicle Technology

This course will introduce characteristics of the future mobility systems and overview of planning, designing, deploying and operating of future connected mobility systems. The advanced multidisciplinary topics include wireless communication technologies, transportation data analytics and traffic simulation. Students will utilize advanced tools to plan and design connected vehicle technology applications. The course is designed for students that are familiar with intelligent transportation system.

ENGR 01.580 Advanced Viscoelasticity

This course covers the fundamentals of linear and non-linear viscoelastic behavior of materials: constitutive modeling, experimental development of material properties, and solution of classic problems. Non-linear viscoelasticity and the effect of temperature on non-linear viscoelastic properties are presented. Standard experimental methods to characterize determine viscoelastic properties are discussed. Classic solutions, and the use of time-temperature superposition of solutions, are presented. In addition, the doctoral students will conduct testing and analysis of viscoelastic material to validate existing viscoelastic models. This course might not be offered annually.

ENGR 01.599: Master’s Thesis Research

This course will provide a meaningful one-on-one research experience under the direction of an engineering faculty advisor. The research topic will be chosen by mutual agreement of the student and his or her adviser. The course will include a thorough literature search and review, the development of a clear and concise problem statement, consultations with other faculty and professional experts, and the derivation of publishable results.

ENGR 01650 Engineering Education Fundamentals

The purpose of this course is to provide students with foundational knowledge about the field of engineering education. As such, students will learn about the history of engineering education and its evolution, the breadth of research areas that are investigated within engineering education, current issues facing the field, and primary stakeholders involved within engineering education.

ENGR 01.660 Research Design in Engineering Education

This course will describe research design in the context of engineering education. It will provide an overview of theoretical frameworks, development of research questions, types of methods (both quantitative and qualitative), as well as discuss research quality. Students will also learn how to critique engineering education research on the basis of its design and research quality. The emphasis will be placed on research design for studies focused on the research to practice continuum.

ENGR 01.700: What is Next in Engineering: Graduate Seminar

One of the primary goals of the Ph.D. in Engineering program is to teach the candidate how to identify unsolved problems in engineering, formulate a hypothesis for a feasible solution, design experiments or analysis methodologies to implement the proposed solution, analyze results and draw conclusions, all of which require critical and analytical thinking and problem solving skills, command of the general body of knowledge as well as state-of-the-art in the area of interest. This is seminar course where every week on graduate student or guest speaker will present the state-of-the-art in his/her area of research interest. This course will allow the presenter to describe an unsolved problem of interest, complete a thorough literature review about related work, and/or present his/her work in that area, and receive comments, suggestions and critical feedback from the audience. The course will also allow audience members to learn a new topic, provide feedback to their peers, and be familiar with the breadth of research taking place in the college, while providing a forum for general exchange of ideas within the college, bringing research active college community together for lively discussions.

ENGR 01.701: Effective Teaching in Academic, Corporate, and Government Settings

This unique course, team-taught by faculty and professionals in education, engineering and industry, provides students with an in-depth exploration of effective teaching practices in academic, corporate, and government settings. Students will gain a broad view of the role and function of teaching and oral presentation, as well as how to communicate effectively in these settings. Specifically, this course will introduce instructional methods and strategies, adult learning theory and implications for effective teaching, documenting and assessing student learning, and how to improve instruction in academic, corporate, and government settings. Several real-world scenarios will be discussed and simulated, including preparing academic courses and corporate training packages, assessing audience background and setting appropriate technical rigor and level, building classroom / meeting room / presentation room management skills, conflict avoidance and resolution in such settings; effective strategies for delivering technical content at meetings and conferences, and answering audience questions that may be adversarial in nature. The course will provide readings, discussions, assignments, and most importantly ample opportunities for practice teaching, including a semester-long apprenticeship with Engineering faculty, allowing the student to experience all aspects of teaching and classroom management. The course will also feature guest speakers from industry and government agencies providing unique perspectives for effective teaching and presentation at such settings.

ENGR 01.702: Strategic Technical Writing and Winning Grant Proposals

Effective technical writing is perhaps one of the most critical skills a Ph.D. engineering graduate needs to have regardless of the career path chosen upon graduation. Whether writing research papers, technical reports, or grant proposals, the ability to convey technical engineering knowledge in an effective, understandable, elegant and concise manner is an important skill. This class will provide the general guidelines, best practices, and most importantly specific strategies for technical writing for some of the most common venues and audiences, namely writing technical papers for engineering conferences and journals - including writing rebuttals to reviewers - technical reports and grant proposals. The latter includes specific strategies for a variety of different sponsors that fund engineering related research, including industrial sponsors, government and military agencies, foundations as well as intra-company funding sources. The deliverables of this class includes an actual conference or journal paper and a small scale grant proposal-ready to be submitted - based on student's area of research.

ENGR 01.799: Doctoral Research and Dissertation

One of the primary goals of the Ph.D. in Engineering program is to teach the candidate how to identify unsolved problems in engineering, formulate a hypothesis for a feasible solution, design experiments or analysis methodologies to implement the proposed solution, and analyze results and draw conclusions. All of these require critical and analytical thinking and problem solving skills. Achieving such a goal requires methodical and persistent effort over a long period of time for obtaining command of the general body of knowledge as well as the state-of-the-art in the area of interest, followed by identifying an unsolved problem that is worth solving, followed by developing and verifying solution(s) and finally disseminating the new knowledge created by this process. Since this is a long term process, it can only be achieved by dedicating significant time and effort to this process. Doctoral Research and Dissertation is a variable-credit independent study based research course that is designed to provide the student necessary time and guidance to help him/her achieve the aforementioned goals. Students are expected to take appropriate number of credits of this class each semester they are materially involved with doctoral research, culminating with preparation, execution, and defense of the Dissertation. Each section of this course is associated with a faculty member, and each student will take that section of this course that is associated with his/her Ph.D. Advisor, who will be guiding the student's doctoral research.

Not all classes are offered every year, whereas additional classes are taught every semester as special and emerging topics under the ECE 09.504 course number. 

^♦ These four courses are also part of the Certificate of Graduate Studies in Combat Systems Engineering (CSE), as well as the MS in Engineering Management with specialization in Combat Systems Engineering. The CSE programs are designed and delivered in cooperation with Lockheed Martin Corporation.