COE Faculty Members Level Up in Applied AI

The first cohort of College of Engineering (COE) faculty members to receive funding from two new award programs launched last year are expanding interdisciplinary research efforts and individual expertise in applied AI.

The first cohort of College of Engineering (COE) faculty members to receive funding from two new award programs launched last year are expanding interdisciplinary research efforts and individual expertise in applied AI.

The Dean’s Applied AI Research Accelerator Award Program provides interdepartmental COE research teams with seed funding to initiate and grow novel research in applied AI.

The Dean’s Applied AI Faculty Scholarly Fellowship Program supports individual faculty members who want to broaden expertise and research capacity in applied AI.

The awardees were selected from over 60 submissions from across the COE. The research topics spanned a broad spectrum including biomedical engineering applications, materials discovery, manufacturing, infrastructure, energy, robotics, and education and workforce development.

These two faculty funding programs are part of the Applied AI in Engineering and Computer Science Initiative, which aims to integrate AI across teaching and research to elevate the college’s research scholarship, transform its teaching and better prepare NC State students for the careers and problem-solving of an increasingly AI-driven future. Launched by Jim Pfaendtner, the Louis Martin-Vega Dean of Engineering, the initiative and these faculty programs equip NC State COE faculty and graduates to become global leaders in developing AI solutions that empower decision-making in complex, real-world situations for individuals, organizations and communities.

Three Faculty Teams Received Dean’s Applied AI Research Accelerator Awards

Project Title: “AI Agents for Battery Materials Discovery and Optimization Development for the Purpose of Developing Non-Toxic Battery Technology”

The objective of this interdisciplinary project is to accelerate the development of new non-toxic battery classes with higher performance and lower environmental footprint than current versions through the implementation of cutting-edge AI frameworks that will optimize four essential criteria: electrochemical performance, cost effectiveness, supply chain resilience and environmental impact. 

Project Title: “Foundations for CORA (Cognitive Operator Readiness Assistant): Knowledge Graph and Prototype Large Language Model for Nuclear Operator Training.” 

Wu and Zhou aim to establish the technical and data foundations for a trustworthy AI system that enhances the training of nuclear reactor operators, starting with NC State’s PULSTAR reactor. The pilot development of the Cognitive Operator Readiness Assistant, or CORA, will demonstrate how emerging AI technologies can scale technical education programs while maintaining the rigorous safety standards essential for high-risk nuclear engineering.

Project Title: “Transforming Nanoscale Sensing with Scalable Agentic AI and Multi-Functional Microscopy”

Xu’s team is developing a scalable agentic AI infrastructure that transforms single-molecule microscopy into an intelligent, automated discovery platform. By integrating multi-agent coordination, uncertainty-aware deep learning, explainable analytics and natural-language interaction, his system enables robust analysis of massive nanoscale imaging data. This AI-driven framework accelerates biomedical research in aging and genome instability while establishing a generalizable foundation for trustworthy scientific AI across engineering domains.

Three Faculty Members Received Dean’s Applied AI Faculty Scholarly Fellowships

Project Title: “Applied AI for Enhancing the Resilience of Water Infrastructure During Disasters”

Berglund’s research will address a fundamental gap in water distribution center infrastructure management, which often relies on retrospective analysis and limits optimal real-time responses during disasters. She aims to develop an AI-enabled sociotechnical framework that will create and update dynamic models of infrastructure performance, consumer behavior and utility decision-making in real-time. 

Project Title: “Applied AI for Intelligent Decision-Making and Optimization in Complex Engineering Systems”

Hajibabai’s research will strengthen the integration of AI and optimization to improve decision-making in complex systems such as logistics, transportation and infrastructure networks. Through advanced graduate-level training, she will develop AI-enhanced frameworks that support adaptive, data-driven engineering systems and drive innovation in research and education. The fellowship will further expand AI integration across engineering curricula and foster interdisciplinary collaboration.

Project Title: “Building AI Expertise to Transform Quantitative Ultrasound and Reveal Hidden Tissue Information”

Muller is working to integrate AI-based signal and image analysis to develop hybrid approaches for tissue characterization that will increase diagnostic potential of ultrasound, an accessible and powerful modality to assess tissue health. She will build on her existing physics-based models that analyze ultrasound signals and will use AI to learn directly from raw ultrasound waves identifying subtle tissue features not captured by conventional methods.