Graduate Programs

PhD in Robotics: The Robotics Ph.D. program prepares students to lead advancements in the design and integration of complex robotic systems. Students will gain the expertise to design cyber-secure robotic platforms, describe and control robotic motion, and incorporate machine learning and artificial intelligence into intelligent autonomous systems. Emphasizing a multidisciplinary approach, the program integrates knowledge from cybersecurity, electrical, and mechanical engineering to achieve cohesive system development. Students also learn to create robotic systems with effective human-robot interaction, conduct critical literature reviews on emerging robotics topics, and develop strong technical writing skills through the completion of a dissertation. Additionally, graduates are equipped to educate and mentor undergraduate and graduate students in the field of robotics.

PhD in Information Technology with a concentration in Mechanical Engineering: Mason Engineering’s PhD in information technology with a mechanical engineering concentration is designed for students interested in pursuing research in a specific area. Doctoral students may choose research in areas as broad as continuum mechanics, or more specific topical areas such as solid mechanics, fluid mechanics, thermodynamics, heat transfer, or robotics. The program prepares students to become leaders at universities, as well as at national and industrial laboratories.

PhD in Systems Engineering and Operations Research with a concentration in Mechanical Engineering: This concentration is suitable for students who wish to pursue doctoral research in areas related to mechanical engineering with a foundation in systems engineering and operations research. The concentration has modified requirements for coursework, qualifying exams, and the doctoral supervisory committee, as described in the relevant sections.

Check the catalog for official information and requirements.

For more information, contact Jeffrey Moran.

The breadth of the mechanical engineering discipline prepares motivated students to pursue an accelerated master’s degree. Up to twelve credits of graduate coursework can be applied towards the completion of both BS and MS degrees allowing a student to earn both degrees in as little as 5 years. Currently, the department administers this degree in several areas including applied and engineering physics, bioengineering, civil engineering, computational science, data analytics, electrical engineering, information technology, operations research, and systems engineering.  For the latest list of programs and requirements, please visit the catalog.

Microfabrication involves the process of creating miniature structures on a micro or nanometer scale. It plays a crucial role in the semiconductor, biomedical, and other industries, being responsible for producing electronic devices such as computer chips, memory devices, and sensors. The certificate program in microfabrication is designed to equip students with the skills needed for the growing job opportunities in this field. Students will gain a solid understanding of advanced materials and sensors, along with hands-on experience. They will also learn about device design principles. Upon completing the certificate program, students will possess the ability to comprehend the unique properties of materials and device performance at the nanoscale. They will be able to fabricate microsensors within a cleanroom environment and operate key instruments used in the cleanroom. Given the increasing demand for well-paying jobs in microfabrication, students holding a microfabrication certificate will enjoy enhanced competitiveness. The training process not only augments their capacities in microfabrication but also complements their STEM majors.

The program consists of four core courses. Details on specific course requirements are available in the catalog. Questions can be addressed to Pei Dong. 

The graduate certificate in naval ship design provides students with the fundamentals and hands-on experience to be effective ship designers and design managers. Through coursework and experiential learning, students will develop specific knowledge in the art of naval ship design and acquire the skills to support the management of new and in-service design programs. Upon completion of the certificate program, graduates will be able to:

• Design a vessel to execute a specified mission profile.

• Critically assess the impact of vessel design decisions on the concept of operations.

• Support programmatic needs by translating fleet requirements to design specifications while managing cost.

The program consists of four core courses plus a student-selected relevant elective course at the 500 level or above. Details on specific course requirements are available in the catalog. Questions can be addressed to Leigh McCue. See application information here.

The purpose of the Graduate Certificate in Responsible Artificial Intelligence is to provide students with the critical knowledge, skills, and analytical abilities needed to identify and address challenges in the design and deployment of systems that incorporate AI. Such systems could include safety-critical physical systems like self-driving cars, air taxis, and health applications, but they also include software-based systems like financial and banking systems, as well as those that support education and research. It is critical that safe levels of performance and adequate testing protocols be defined for each of these systems, as well as the infrastructure that will be required to develop and maintain such systems.

Students will learn the fundamentals of artificial intelligence, how AI systems are architected, the principles of systems engineering as they relate to AI systems, theories of AI safety and risk, how to test and evaluate such systems to meet risk thresholds, and how to identify ethical, legal, and regulatory issues that arise in such systems. Students will be prepared to develop and manage complex systems with embedded AI, including identifying unique requirements for systems with embedded AI, testing and certifying these systems, and defining and maintaining safe levels of performance for deployed AI. Testing is especially important in safety-critical systems with embedded AI, like self-driving cars and drone delivery.

Graduates of this certificate program will be able to develop acquisition plans for complex systems with embedded AI, work with computer scientists to develop AI, develop and execute test plans for AI that contain clear performance gates, develop AI maintenance programs including auditing, and identifying those legal, ethical, and/or regulatory issues that may be a barrier to system implementation.

The certificate program will be offered on a full-time and part-time basis in a face-to-face format, although online options may be available in the future. Students may begin the proposed certificate program in either the fall or spring semester. Degree-seeking students can complete the program in tandem with the degree program in which they enrolled. The length of time to complete will be based on scheduling within their degree program. Students can complete the certificate program in one academic year (two semesters) if maintaining a course load of 9 credit hours one semester and 5 credit hours the second semester.

Non-degree-seeking students can complete the certificate program in one academic year (2 semesters) if maintaining a course load of 7 credit hours per semester. Students can complete the certificate in one and a half academic years (3 semesters) if maintaining a course load of 6 credit hours for the first semester and 4 credit hours for the second and third semesters.

Admission

All applicants must:

• Submit a completed online application for graduate study.
• Submit a nonrefundable application fee.
• Submit one unofficial transcript from all institutions previously attended. 
• Have earned a baccalaureate degree in computer science or in a discipline of engineering
• such as mechanical, electrical, or systems engineering. Applicants seeking to enroll without a degree meeting this requirement must seek approval, in writing, from the program director.
• Have earned a minimum 3.00 GPA on a 4.00 scale in baccalaureate study.

Students who have not earned a baccalaureate degree in the U.S. must submit:

• Official English translations of all diplomas, certificates, and transcripts that are not already in English. Also, documents from foreign institutions must meet the university’s guidelines for international transcript submission.
• Proof of English proficiency: either the Test of English as a Foreign Language (TOEFL), the International English Language Testing System (IELTS) academic exam, or the Pearson Tests of English (PTE), meeting the minimum requirements:
  • TOEFL: 88 points total and a minimum of 20 points in each section for the internet-based test (IBT) or 570 for the paper-based test (PBT)
  • IELTS: 6.5 total band score
  • PTE: 59 overall score.

Program Requirements

14 credit hours

• SYST 568 Applied Predictive Analytics
  • or CS 580: Introduction to Artificial Intelligence (3 credits)
  • or ECE 527: Learning from Data (3 credits)
  • or SYST/OR 664 Bayesian AI
• ME 575: AI Design and Deployment Risks (3 credits)
• ME 576: AI: Ethics, Policy, and Society (3 credits)
• ME 577: Emerging AI Robotics Tech Seminar (1 credit) (Repeated for 2 credits)
• SYST 578: Systems Engineering and Artificial Intelligence (3 credits)

SYST 568 Applied Predictive Analytics (3 credits)

Introduces predictive analytics with applications in engineering, business, health care, marketing, and social economic areas. Topics include cross-sectional data processing, data visualization, correlation, linear and multiple regressions, classification and clustering, factor models, and predictive modeling performance analysis. Provides a foundation of basic theory and methodology with applied examples to analyze large engineering, social, and econometric data for predictive decision making. Hands-on experiments with R will be emphasized.

CS 580: Introduction to Artificial Intelligence (3 credits)

Artificial Intelligence principles and methods. Topics will include uninformed search, informed search, adversarial search, probabilistic reasoning and models, Bayes networks, machine learning fundamentals, classification and clustering, and neural networks. Additional topics may include knowledge representation, constraint satisfaction search, agent architectures, and Markov decision problems, among others. Offered by Computer Science. May not be repeated for credit. Recommended Prerequisite: CS 310 and CS 330.

ECE 527: Learning from Data (3 credits)

This is an introductory course in machine learning and pattern recognition that covers basic theory, algorithms, and applications. Machine learning is the science of getting computers to act without being explicitly programmed. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. It provides a broad introduction to machine learning and pattern recognition. Topics include: (i) supervised learning (parametric/nonparametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, autoencoders). (iii) Learning theory (bias/variance tradeoffs, VC theory, generalization). (iv) Ensemble methods (boosting and bagging, random forests). (v) Deep learning (deep belief networks, convolutional neural networks, deep autoencoders). The course will draw from numerous case studies and applications. Offered by Electrical & Comp. Engineering. May not be repeated for credit. Equivalent to DAEN 527. Recommended Prerequisite: (MATH 203 and STAT 346) or equivalent.

SYST/OR 664 Bayesian AI (3 credits)

Many artificial intelligence problems involve modeling uncertainty. Bayesian probabilistic models represent uncertainty and dependencies between random variables using probability distributions. You will learn the set of rules of probability and computational algorithms to manipulate these distributions. Bayesian approach enhances the effectiveness of conventional AI techniques. This course summarizes various Bayesian-based models and the standard algorithms used with them, supplemented by instances of their practical use. We will discuss applications in science, engineering, economics, medicine, sport, and law. Introduces decision theory and relationship to Bayesian statistical inference. Students will learn the commonalities and differences between the Bayesian and frequentist approaches to statistical inference, how to approach a statistics problem from the Bayesian perspective, and how to combine data with informed expert judgment soundly to derive useful and policy-relevant conclusions. Assignments focus on applying the methods to practical problems.

ME 575: AI Design and Deployment Risks (3 credits)

This course will explore the fundamental issues that underpin risk inherent in systems that utilize AI. Students will learn how to measure these risks, assess the impacts and harms that could result from AI, and formulate plans for managing risks including testing, maintenance, governance and legal interventions. Topics will include AI robustness, generalizability, validity, reliability, safety, and security and students will develop risk assessment plans for a domain of their choice. Offered by Mechanical Engineering. Recommended Pre/Co-requisites: Pre/Corequisite: ECE 527 or CS 580.

ME 576: AI: Ethics, Policy, and Society (3 credits)

Artificial intelligence (AI) technologies are rapidly expanding across multiple domains, leading to significant debate about its ethical and societal impacts. Still a matter of debate is what appropriate legal and governance structures should be created to ensure the ethical design, development, deployment, and use of AI. Further complicating the debate is the question of which parties and stakeholders should contribute to creating AI governance structures and mechanisms. The course will explore pressing issues in ethics and policy, including transparency, privacy and surveillance, misinformation and disinformation, fairness, algorithmic bias (from both underlying data and modeling choices), justice, equity, trust, and labor practices and supply chains. These topics will be grounded in specific use cases often drawn from cutting edge topics in the news. Offered by Mechanical Engineering. No prerequisites.

ME 577: Emerging AI Robotics Tech Seminar (1 credit)

Focuses on student-led critical analyses of emerging AI issues in the news across all domains. Speakers from industry and government will also participate. Two semesters of this seminar are required to graduate. Offered by Mechanical Engineering. No prerequisites.

SYST 578: Systems Engineering and Artificial Intelligence (3 credits)

This course provides a foundation for systems engineers to understand the implications of both building systems with artificial intelligence (SE for AI) and using artificial intelligence to enhance the systems engineering process (AI for SE). The course introduces the foundations of AI, including different types of machine learning, and the associated design, test, and evaluation challenges for AI systems. AI opportunities for transforming SE lifecycle activities are discussed along with applications of AI in modern systems. Offered by Systems Engr & Operations Rsch. May not be repeated for credit. Recommended Prerequisite: SYST 520 or permission of instructor.

Certificate Cost

$9,843 for 14 credit hours in state ($692/credit + $155 in fees)

$20,966 for 14 credit hours out of state ($1,486.50/credit + $155 in fees)

All classes in the certificate can count towards earning an advanced degree.

Details on specific courses and their requirements are available here. Questions can be addressed to marc@gmu.edu. See application information.