People
Mission statement for ICL.
Who We Are
We are a diverse, international, and collaborative team of researchers in the Department of Industrial and Systems Engineering and Engineering Management (ISEEM) at the University of Alabama in Huntsville. We believe that science is a team sport, and we support one another in thriving both within and beyond academia through formal and informal mentorship. Our shared goal is to create and share globally relevant knowledge in design and manufacturing - fields that deeply fascinate us - while inspiring similar enthusiasm in students, collaborators, and the public. We strive to build AI-driven systems to address complex challenges in design and systems science. Positivity, balance, respect, passion, curiosity, integrity, and inclusiveness define our lab culture. Above all, we pursue design, systems, and manufacturing research with a commitment to equity, ethical practice, and genuine partnership with our communities.
What We Do
We are driven by the belief that intelligent, trustworthy, and transparent computational tools can empower engineers to design safer, more sustainable, and more resilient systems. In the Integrated Computing Lab (ICL), we study how machine learning models can understand and capture complex behaviors in physical and engineering systems. To do this, we design hybrid model architectures that couple data-driven networks with simulation-based physics, enabling the development of trustworthy digital artifacts for aerospace, manufacturing, and energy systems. How can we integrate physics-based and data-driven models to improve manufacturing processes, decision-making, and trade-off analysis? What are the most effective model architectures and mathematical representations for capturing complex systems and addressing ill-defined design challenges?
Ph.D. and Master's Students
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This is a much longer bio to test how the layout handles extended text content. This researcher focuses on machine learning applications in manufacturing systems, with particular emphasis on computer vision for quality control and defect detection. Their work spans multiple domains including aerospace manufacturing, automotive assembly lines, and semiconductor fabrication. They have published numerous papers in top-tier journals and conferences, and have collaborated with industry partners on several funded research projects. Prior to joining the lab, they completed their undergraduate degree in mechanical engineering and worked as a systems engineer for three years. Their current research interests include deep learning architectures for time-series analysis, physics-informed neural networks, and the integration of simulation-based methods with data-driven approaches for predictive maintenance and process optimization.
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Undergraduate Students
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