Upcoming Events

The availability and access to an ever-expanding list of AI tools for faculty and students is driving a significant change in how we teach and learn. During this session we will gather feedback on the types of AI tools faculty members are already using or plan to use to make them more effective educators, discuss how we might manage the expansion of AI tools in teaching, and brainstorm how to ensure our students use AI tools in the classroom ethically.

AI is transforming countless aspects of society, fundamentally and dramatically impacting all areas of engineering and computer science. Faculty, students, and graduates of all engineering and computer science disciplines must be able to understand and harness its potential to remain competitive and contribute to the advancement of their fields. This shift requires evolving the engineering curriculum. In this session, participants will discuss which AI-related topics all engineering students should learn and how to structure a learning path that provides both a broad AI knowledge and depth of Applied AI within engineering disciplines.

Just as AI is transforming teaching and curriculum, it is creating a change in the spaces where learning occurs. Growth in the College, aligned with Engineering NC’s Future, is being fully informed by the Applied AI initiative. New buildings aligned to expansion will not be department specific but will be designed around collaborative Applied AI research and teaching. Instead of traditional classrooms and research labs, we can envision new instructional spaces that allow for experiential learning and further integrate teaching into research. Participants in this session will provide feedback on this future space and the resources needed – giving input on how to structure and equip new instructional spaces.

The multidisciplinary field of Robotics and Automation encompasses the design, development, and application of robots and automated systems. Already in use across a wide spectrum of industries including manufacturing, healthcare, and agriculture, the rapid advancement of applied AI presents the opportunity to improve robots’ ability to make complex decisions and adapt to dynamic environments.

Focused on developing and refining technologies that enable systems to operate intelligently and seamlessly integrate with each other, research in Smart Systems and Integration has application across multiple domains including smart cities, healthcare, and transportation. New technologies are expected to present opportunities for researchers to examine how technologies like advanced AI will enhance the capabilities and efficiency of smart systems and their integration processes.

The field of sensors and measurement is critical for gathering data and making informed decisions in a wide range of applications. With the growth of AI, the need to make sensors smaller, more energy-efficient, and more cost-effective while fusing their information across modalities to enable widespread adoption and integration into various applications is increasing. Research in this area will push the boundaries of what is possible in detecting and quantifying physical phenomena, enabling better data collection, analysis, and application across numerous fields.

Materials and macromolecules research is broad and interdisciplinary focused on understanding, developing, and applying different materials and macromolecular substances. Research is crucial for advancing technology, improving quality of life, and addressing global challenges. The application and use of AI in materials and macromolecular research is enhancing predictive capabilities, optimizing processes, and uncovering new insights that will drive innovation across various scientific and industrial domains.

Research in fundamental AI algorithms and theories involves exploring and developing the core principles and techniques that underpin artificial intelligence systems. This research is foundational to advancing AI technology. Areas of research include advanced machine learning algorithms, algorithmic fairness and ethics, algorithmic interpretability and explainability, and AI safety. Research in these areas is crucial for advancing the state of AI, improving the performance and application of AI systems, and addressing ethical and societal challenges associated with AI technology.