Ph.D. in Electrical and Computer Engineering

André Gide, the 1947 Nobel Prize laureate in Literature, once said that “Man cannot discover new oceans unless he has the courage to lose sight of the shore.“ This is a poetical but nonetheless true thought on the process of discovery. Yet, with our engineer’s pragmatic thinking, we would add that to succeed in the discovery process, we not only need courage, but we also need knowledge (after all, a sailor needs to know navigation to venture beyond the sight of the shore!) The curriculum for the Ph.D. in electrical and computer engineering provides the knowledge and skills to develop successful independent researchers by providing disciplinary and interdisciplinary courses, research mentorship, and seminars.

Core courses: Core courses are usually completed during the first two semesters of the program since they serve as a foundational preparation for elective courses, developing core competency skills for research, introducing the research landscape in electrical and computer engineering, and helping prepare for the qualifying exam.

Discipline Concentration Elective Courses: The discipline concentration elective courses provide a rigorous education in a student’s field of research in electrical and computer engineering. Students may choose elective courses in consultation with the dissertation and research advisor, and from courses offered by the department of electrical and microelectronic engineering or the department of computer engineering.

Focus Area Elective Courses: Focus area elective courses provide the curriculum flexibility for students to engage in trans-disciplinary learning. Students, in consultation with the dissertation and research advisor, graduate courses offered by any of the departments in the Kate Gleason College of Engineering. In addition, and subject to the program director’s approval, students can choose graduate courses offered by any of the RIT colleges.

Qualifying Exam: Students complete a qualifying exam at the end of their first year of study. The exam evaluates the student's aptitude, potential, and competency in conducting Ph.D.-level research.

Dissertation Proposal and Candidacy Exam: Students must present a dissertation proposal to their dissertation committee no sooner than six months after the qualifying exam and at least twelve months prior to the dissertation defense exam. The proposal provides the opportunity for the student to elaborate on their research plans and to obtain feedback on the direction and approach to their research from his/her dissertation committee.

Research Review Meetings: Research review meetings provide comprehensive feedback to the student regarding their dissertation research progress and expected outcomes prior to the defense of their full dissertation. Research review meetings must be held at least every six months following the conclusion of the dissertation proposal and candidacy exam until the dissertation defense.

Dissertation Presentation and Defense: Each doctoral candidate prepares an original, technically rigorous, and well-written dissertation that describes the candidates’ research body of work and novel contributions that have resulted from their doctoral studies in the discipline of electrical and computer engineering. Each doctoral candidate presents and defends their dissertation and its accompanying research to their dissertation committee.

Research

The advancement of world-class impactful research is the ethos of the Ph.D. in electrical and computer engineering. Our faculty and students work every day to bring the next wave of transformational advances for our information age society by doing research in any of the following four areas:

• Architectures and Devices for Computing
• Communications, Networking, and Security
• Machine Learning and Artificial Intelligence
• Cyber-physical and Embedded Systems

Architectures and Devices for Computing

Our information age is built on the foundation of the devices that process and store information. Our faculty and students conduct research into computer architecture and computing devices that will bring the next technological advances to our information age. Research projects in this area include energy-efficient device architecture, optoelectronic devices, reconfigurable hardware, networks-on-chips, heterogeneous computing, future computing devices for late and post-silicon technologies, and computing devices based on the emergent revolutionary computing paradigms of quantum computing and neuromorphic computing. In addition, research in this area includes emergent paradigms that blend computing and networking architectures together, as is the case with Edge Computing.

Communications, Networking, and Security

It is no accident that our digital world’s currency, the “bit”, originated with the “Theory of Communication,” the work from Claude Shannon that gave birth to the field of information theory. Our information age depends on the ability to communicate information securely. At RIT, our faculty and students are dedicated to researching multiple aspects of communications and networking technology. Research projects in this area involve the study of such diverse issues as 5G and B5G (beyond 5G) communications, electromagnetics of wireless networks-on-chips, theoretical modeling, and measurement of microstrip antennas and integrated microwave circuits, Wearables, and Wireless Body Area Networks (WBAN), cognitive radios and networks, dynamic spectrum sharing, MIMO wireless communications, and advanced fiber-optics networks.

The exchange of information leads to the need to secure the communication links, the networks, and the computing systems they interconnect. As such, our faculty and students also conduct research into Wireless Physical Layer Security (WPLS), modulation obfuscation, cryptographic engineering, connected vehicles (V2V) security, IoT security, and predictive cyber situation awareness.

Machine Learning and Artificial Intelligence

Within the past decade, advances in computer architecture and computing power have led to giant strides in the development of computers that are capable of learning by themselves how to solve a problem and of applications that make use of this ability. These technologies, collectively known as machine learning or artificial intelligence, are engendering new revolutionary technologies and new approaches to solve difficult contemporary problems. Our faculty and students are actively involved in this process of revolutionary creation with projects in areas that include:

• Neuromorphic devices and circuits, and brain-inspired architectures and algorithms for energy-efficient AI
• Tensor methods for deep learning and tensor analysis of big and multi-modal data
• Applications of machine learning to wireless communications, network management, and dynamic spectrum access, sharing, and sensing
• Reliable learning in adversarial environments and trustworthy AI hardware
• Deep fake detection
• Self-driving vehicles
• Smart warehouses
• Computer vision, object recognition, and tracking
• Human-Robots interaction and collaboration
• Deep learning algorithms for machine intelligence and AI applications
• Biologically inspired learning models for multi-agent and complex systems
• Object classification and localization via quantized neural networks

Cyber-Physical and Embedded Systems

One of the most revolutionary applications of electrical and computer engineering technology is when it is coupled to a physical system, forming a close loop with sensors, computing elements, and electrically operated actuators to control the operation of the physical system. Examples of these cyber-physical systems are everywhere, as a part of the Internet-of-Things, with the computing elements being embedded in everyday objects, from coffee machines to cars. Some of the research being conducted in this area is at the scale of the very small, where microelectromechanical systems (MEMS) are being investigated for integrated sensing, control, energy harvesting, and multi-sensor networks. At the scale of large physical systems, they may encompass complete infrastructures. In this area, our faculty and students are researching technology for smart warehouses and Industry 4.0.

An important element in this area of research is that of energy systems. Ongoing research in this area includes power system optimization, grid integration of renewables (wind and solar), operation optimization of microgrids and distributed energy systems, and scheduling of manufacturing systems. Other research projects investigate the interdependency between the smart grid and other infrastructures (, for example, telecommunications or computing infrastructure).