Data, Decisions, Networks and Autonomy

About D2NA

With the ever-expanding capabilities of cyber-physical systems—data and sensor technologies; communications, social, biological and other networked systems; and information and decision sciences, the Department of Electrical and Computer Engineering (ECpE) recognizes that Data, Decisions, Networks and Autonomy play fundamental roles in designing complex engineering systems.

Number of (semi)-autonomous, heterogeneous, distributed, collaborative entities interacting across networks/grids through corpus of noisy, incomplete, real-time/streaming/historical data, signals and dynamics requires advances at the interplay of Data, Decisions, Networks, and Autonomy Sciences.

Advancements in these areas promise accurate and optimized sensing, data and information processing, communications and networking, decisions and autonomy, and Iowa State researchers aim to be on the cutting-edge of such advancements.

D2NA Faculty

D2NA Focus Areas and Applications

  • Data analytics, social and biological networks, machine learning, robotics
    • Large scale and streaming data, and video analytics
    • Social network security, bioinformatics and systems biology
    • Behavioral and unmanned robotics and autonomy
  • Compressive sensing, signal/image processing, and communication
    • Sparse and low-rank robust signal recovery
    • Nano-imaging methods and materials/fatigue characterization
    • Distributed multi-antenna communication and channel network coding
  • Complex nonlinear, networked and cyber-physical systems
    • Distributed and networked computing, control and feedback
    • Model-based compositional testing and verification of embedded software
    • Sensors and methods for sustainable agriculture and energy harvesting

Research conducted in this area spans the domains of data to decisions with core strengths in mathematical modeling and algorithmic analysis, and finds applications in diverse disciplines of big data, signals processing and storage, information systems and coding, statistical reasoning, nondestructive evaluation, machine learning, biomedical imaging, radar and ultrasound, software defined radio, systems biology, biomedical systems, embedded and model-based software, formal methods, verification and testing, autonomous systems, robust control, robotics, smart grid, building automation, precision agricultural, energy harvesting, and critical infrastructure.

D2NA Highlights

Ratnesh Kumar, professor, was elected a Distinguished Lecturer for the Control Systems Society of the IEEE to share his work on model-based testing and monitoring of embedded software.

  • Ratnesh Kumar, professor, was featured in IEEE Control Systems Magazine under the “People in Control” column to provide an interview on his Controls career, research and educational contributions, textbook authoring, etc
  • Zhengdao Wang, professor, was named an IEEE Fellow for his contributions to thefield of wireless communication. His work centers on wireless security, communication and networking for the Internet of Things (IoT), and big data signal processing.
  • Ratnesh Kumar, professor, elected IEEE Fellow for his contributions to discrete event system modeling, control, diagnosis and applications.
  • Iowa State University was ranked 70th in the world among universities granted U.S. utility patents in 2014, many of whom are ECpE faculty, including the D2NA group members
  • Ratnesh Kumar, professor, and his team won a $1 million grant from the National Science Foundation to develop underground wireless sensors that could help farmers improve crop production and reduce fuel runofff.
  • Umesh Vaidya, associate professor, won an NSF CAREER Award to promote efficiency in networked systems using ergodic theory of dynamic systems
  • Aditya Ramamoorthy, associate professor, won an NSF CAREER Award to work with readouts in Atomic Force Microscopy using coding theory.
  • Nicola Elia, professor, won an NSF CAREER Award for his focus on networked systems and developing new analytical and computational tools for understanding and analyzing how uncertainty is generated, propagated, and managed in the system.

Related Graduate Research Areas

Data, Decisions, Networks & Autonomy research at Iowa State encompasses the following core areas of graduate study in electrical and computer engineering:

Related IEEE Technical Chapters

Research Centers, Institutes, and Laboratories


For more information on research projects and publications in this area, consult individual faculty members’ websites, our graduate student thesis archives, and individual research center and laboratory websites.