SIE Graduate Programs

The University of Virginia’s Department of Systems and Information Engineering is engaged in innovative research, dynamic teaching, and enriching engagements with partners in industry, government, and civic society. Together, we advance the principles of the discipline and its practice across a broad range of applications that consider the role of humans in engineered systems.


Our dynamic education includes over 26 core and elective courses in Systems Engineering, and the ability to take graduate elective courses throughout the School of Engineering and Applied Science, and other schools across the university, including the School of Architecture, Curry School of Education, School of Medicine, and the Graduate School of Arts and Sciences. These electives may be tied to collaborative research between SIE and these other schools. The department also partners with SEAS to offer competitive PhD teaching fellowships so students can experience crafting and co-teaching a course with a supervising member of the faculty. Students also have access to the resources of the Center for Teaching Excellence to enhance their teaching skills.

Our research engagements include industry partners such as Mitre and Rolls Royce. Government partners include the Department of Defense, Department of Homeland Security, and the Virginia Department of Transportation. Our Civic Society partners include the American Red Cross and the Building Goodness Foundation. These partnerships provide opportunities for students to work in a variety of research laboratories and centers in the University, at the partner organization, and in the field, on projects in the U.S. and abroad.

Our program is driven by six core, overlapping research areas:
  • Computational Statistics and Simulation

    Research in the Computational Statistics and Simulation group involves modeling, analyzing, and simulating dynamic systems characterized by complex process logic and uncertain behaviors. 

    Forecasting and Simulation Group

    Research in the Forecasting and Simulation group involves modeling, analyzing, and simulating dynamic systems characterized by complex process logic and uncertain behaviors. Methodological interests in these areas include data-mining, response-surface, time-series, and simulation-optimization methods; spatial-temporal data analysis and pattern recognition; and Monte Carlo and discrete event simulation. Technological components concern the integration of decision and information sciences to address the representation, storage, dissemination, processing, analysis, and interpretation of large data sets. These tools are applied in a variety of areas, such as computational finance, environmental monitoring, object/target recognition, statistical signal and image processing, crime analysis, health care delivery, logistics and distribution, manufacturing, and aerospace systems.

    Related Websites:

    Complex Systems Modeling Lab
    Predictive Technology Laboratory

    Primary Faculty:
    Donald E. Brown
    Matthew S. Gerber
    Roman Krzysztofowicz

  • Human Factors

    Human Factors

    Human Factors investigates the cognitive and physical capabilities of humans at work, analyzing both individual and team behavior at different levels of abstraction in order to quantify and predict human performance. We also develop methods and tools to study human performance in the laboratory and in the field. The results inform the design and construction of user interfaces, physical equipment, and training interventions. Research and industrial applications include:

    • Team communication and coordination studies
    • Medical simulator design and construction
    • Methods for analysis such as eye-tracking and video analysis
    • Computational models of the neural basis of touch for neural prosthetics
    • Design of tools and techniques to improve human performance

     

    Primary Faculty:
    Gregory Gerling
    Stephanie Guerlain
    Inki Kim

  • Risk and Decision Analysis

    Risk and Decision Analysis

    The Center for Risk Management of Engineering Systems develops theory, methodology, and technology to assist in the management of risk for a variety of engineering systems. 

    Related Website:

    Center for Risk Management of Engineering Systems

  • Systems Analysis, Design, and Integration

    Systems Analysis, Design, and Integration

    Systems integration is a fundamental and critical aspect of systems engineering. Systems integration can involve the integration of any system components, including hardware, software, and policy. Also, it can involve "systems of systems", i.e., the integration of disparate systems.

    Example Center. WICAT (Wireless Internet Center for Advanced Technology) is an NSF-Sponsored Industry/University Cooperative Research Center. UVa is one of three research university sites that constitute the overall Center. The other sites are Columbia University and Polytechnic Institute of New York. The UVa research site is directed by Barry Horowitz, Professor of Systems and Information Engineering. Research is principally funded by industry, with supporting funding from both UVa and NSF. The UVa site focuses its research efforts on rapidly reconfigurable wireless systems. The Systems Technology Integration Laboratory permits significant experimental support to the UVa site's technology development efforts. Currently the UVa site is engaged in collaborative efforts with a number of companies, including a mixture of larger and smaller companies, systems integration companies and component technology development companies. The collaborations include efforts with Lockheed-Martin, Accenture, Northrup/Grumman, Mitre, Cisco, the Virginia Transportation research Center, and others. Research has included efforts targeted at wireless sensor networks and on integrating large scale enterprise systems with mobile users and wireless information providers.

    The research has included exploring:

    1. Advanced image compression technology for dynamic adjustments of delivery based on wireless communications congestion,
    2. Utility-based information management concepts,
    3. Game-theory based sub-system coordination concepts,
    4. Automated power management of wireless components based on overall system-level considerations,
    5. Application of advanced peer-to-peer networking technology for controlling situational information distribution,
    6. AI-based automation for supporting humans to rapidly reconfigure information flows and the corresponding network configurations, and
    7. Automated self-testing of configuration for wireless sensor networks.

     

    Mission areas that have been the focus for applying the Center's technology efforts include military systems, robotic systems, homeland security systems, and ground transportation systems.
    Example project: Vehicle-Infrastructure Integration (VII) is defined as creating an enabling wireless communication infrastructure to support vehicle-to-vehicle and vehicle-to-infrastructure communications for safety and mobility applications. The full integration of vehicles and infrastructure has long been a vision of the surface transportation system, but lack of available enabling technology has prevented this vision from turning into reality. However, recent developments in information technology have led to the development of Dedicated Short Range Communications (DSRC). DSRC is a short to medium range communication technology that supports public safety and private operations in roadside-vehicle and vehicle-vehicle communications. DSRC is a complement to cellular communications providing high data rates, and it is useful when isolating relatively small communication zones is important.

    The group's areas of expertise in systems integration include:

    • Transportation systems integration
    • Wireless systems integration

    Courses:
    Recommended Core: SYS 6001, SYS 6002, SYS 5581
    Other recommended courses: SYS 6070, SYS 6050, SYS 6023, and SYS 6005

    Primary Faculty:
    R. Reid Bailey
    Peter A. Beling
    Garrick E. Louis

  • Optimization and Control

    Optimization and Control

    The development of optimization and control techniques for improving system performance is the main thrust of this research group. Typically, the research begins with the development of a mathematical model that captures the main traits of the system of interest. Subsequently, algorithmic schemes aimed at optimizing and/or controlling selected measures of performance are designed and implemented. Finally, extensive experimental tests are conducted in order to benchmark the benefits of the proposed schemes.

    Research in Optimization and Control at the Department of Systems and Information Engineering at the University of Virginia has particular strengths in three methodological areas.

    • Stochastic control: Markovian decision processes, stochastic dynamic programming.
    • Decentralized algorithms for large-scale system optimization: complex network optimization, multi-agent coordinated control.
    • Dynamic games: economic regulations of network industries.

     

    The group's research activities are tightly coupled with various research centers at the University of Virginia, including WICAT (Wireless Internet Center for Advanced Technology), ROMAC (The Rotating Machinery and Controls Laboratory), and the UVa Diabetes Technology Program.

    Primary Faculty:
    Peter A. Beling
    Stephen D. Patek

  • Unstructured Information Analysis

    Unstructured Information Analysis

    This research develops methods to automatically identify structured information within unstructured text.

    Many systems can be accurately characterized using direct observation mediated by electronic sensors. For example, one can collect accurate data for systems ranging from patient health (e.g., blood pressure monitoring) to transportation infrastructure and social networks. However, many human-oriented systems also contain a tremendous amount of information stored as unstructured text. In all such systems, the complexity and ambiguity of natural (i.e., human) language limit our ability to perform automated observation and analysis. Our research removes these limitations by automatically identifying structured information within unstructured text. Research in this area has included the following:

    • Predicting future crime based on analyses of social media content
    • Extracting medical knowledge from clinical practice guidelines for automated decision support
    • Detecting terrorist recruitment in online discussion forums

     

    Primary Faculty
    Donald E. Brown
    Matthew S. Gerber

We offer multiple graduate degree options:

Doctor of Philosophy (PhD), Master of Science (MS), and Master of Engineering (ME) degrees.  The PhD and MS degrees are research-based, with a culminating dissertation or thesis, respectively.  The ME degree has a supervised research project.  The PhD and MS degrees have a residency requirement (one must be physically present to attend classes and conduct research).  The ME program on-grounds has a residency requirement as well, but this is only one of three routes to the ME degree.  The ME can be achieved on-grounds, through the distance learning program Commonwealth Graduate Engineering Program (CGEP), or through our popular weekend-based, Accelerated Masters Program (AMP).

Whether your interests are in cyber-physical systems, sustainable infrastructure, robotics or human development, we cover the full range of human-engaged engineered systems, and we invite you to join us in improving the world.

Elizabeth Connelly, PhD, 2016

"The department brings together students from diverse backgrounds and provides them with methods that they can use to solve real-world problems in a wide variety of fields."

Elizabeth Connelly, PhD, 2016