Students and faculty in the Department of Electrical, Computer, Software, and Systems Engineering are some of the more prolific researchers in the Embry-Riddle family. The department's research expenditures are nearly one-half those of the entire College of Engineering, with support from federal agencies including NSF, FAA, and NOAA as well as industry partners. The department is heavily involved in projects managed by ERAU's NEAR Lab and by the COE's Eagle Flight Research Center.

Strategic department research directions include three areas critical for the future of aerospace. These are:

  • Detect and avoid technologies for unmanned aircraft systems;
  • Assured systems for aerospace, including cybersecurity and development assurance;
  • Modeling and simulation for aviation and aerospace.

Detect and avoid technologies enable unmanned aircraft systems to "see and be seen" by other aircraft and by air traffic controllers on the ground. Of particular challenge is detect and avoid of uncooperative aircraft, those aircraft that aren't equipped to announce their position either automatically or in response to interrogations from the ground.

Assured systems are those that are robust in the face of cybersecurity challenges, with assured development being system design approaches that yield assured systems without high overhead.

Modeling and simulation for aviation involves everything from the logistics of getting passengers onto aircraft to planning how to get all air traffic around predicted bad weather without upsetting arrival times and locations.

Machine Learning Engineering: Infusing Software Engineering through the Semantic Web

PI Omar Ochoa

​The Semantic Web provides a wealth of high-quality, structured, and contextual data, which can be used to train machine learning models.

The Semantic Web provides a wealth of high-quality, structured, and contextual data, which can be used to train machine learning models. This can lead to the creation of models, i.e., the engineering of Machine Learning, that adhere to non-functional requirements, which include considerations such as safety, security, and reliability, which are key elements of Software Engineering. These requirements do not concern a system's functionality, but rather its quality attributes. By incorporating these concepts into the engineering of machine learning models, one can strive to create models that are secure, reliable, and exhibit the desired quality attributes. Furthermore, Verification and Validation, or V&V, is integral to successful software engineering, by ensuring that a system is implemented correctly and meets specified requirements. In engineering Machine Learning, it's equally important to define processes and methods to thoroughly test and validate models to ensure they're performing as expected and providing accurate results. Together, the fusion of Software Engineering principles into Machine Learning Engineering, aided by the Semantic Web's capabilities, can bolster trustworthiness in machine learning systems. This trustworthiness ensures that the systems can be relied upon to behave as expected. In essence, by combining these fields, one can develop machine learning systems that are reliable, secure, interpretable, and trustworthy, upholding the core principles of Software Engineering. Our research group focuses on the most recent developments in these areas, i.e., Knowledge Graphs and Large Language Models, to accomplish these goals.

Researchers

  • Omar Ochoa

    Omar Ochoa

    Department
    Electrical Engineering and Computer Science Dept
    Degrees
    Ph.D., M.S., B.S., The University of Texas at El Paso