AI Systems
Bridging AI and real-world challenges to drive efficiency and opportunity
UT researchers in AI systems form the bridge between the fundamentals of AI—such as the computing architectures, statistical models, and algorithms that undergird machine learning—and positive outcomes for science, industry, and humanity.
Faculty design and build intelligent systems by coordinating the interactions between distinct AI elements such as machine learning pipelines, autonomous agents, edge AI, and AI-driven simulations. Researchers continue to optimize these systems for accuracy, efficiency, flexibility, and scalability, allowing them to transform workflows, enable process efficiency, and shape decision-making in applications from astronomy to zoology.
UT’s Approach
UT researchers are integrating AI, machine learning, high-performance computing, and cloud platforms to tackle challenges in large-scale data distribution, storage, and computation, making AI workflows faster, more efficient, and more trustworthy. They are advancing autonomous systems across domains—from globally connected microscopy networks to air mobility to autonomous driving models that improve safety and reduce congestion. In agriculture, faculty combine computer vision, Internet of Things devices, and deep learning to strengthen food security with farm-specific digital twins, robotic pest monitoring, and automated pollination systems. In health and human services, they are developing socially assistive robots for dementia care, AI-enabled surgical systems, and diagnostic tools for conditions like atrial fibrillation. By uniting machine learning frameworks, physical mechanisms, and control systems, UT is creating intelligent robots capable of operating in unstructured environments, expanding the reach of AI into discovery, mobility, agriculture, and health care.
UT has moved quickly and early in the emerging high-potential field of fully automated materials synthesis. The university has invested in our work building AI-enabled systems and workflows. With these unique operational capabilities, UT can truly make an impact on the state, country, and world.”
Sergei Kalinin, Weston Fulton Professor of Materials Science and Engineering, UT; Chief Scientist, AI/ML for Physical Sciences, Pacific Northwest National Laboratory
Highlights
Facilities & Initiatives
Faculty study and build AI systems in state-of-the-art facilities on and off campus. Centers and research groups receive support from and partner with organizations including the National Science Foundation, the US Department of Energy, the US Department of Transportation, and industry leaders.
- Center for Materials Processing
- Center for Nanophase Materials Sciences at Oak Ridge National Laboratory
- Infrastructure for Scientific Applications and Advanced Computing (ISAAC)
- Institute for Advanced Materials and Manufacturing
- Materials Research Science Center, Center for Advanced Materials and Manufacturing
- Oak Ridge Leadership Computing Facility
Researchers
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Associate Professor, Chemical and Biomolecular Engineering
Computation, simulation, theory, cell biology, immunology, intracellular transport, synthetic biology, systems biology, biomembrane, cytoskeleton, nanoparticle, biophysics, machine learning
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Professor, Materials Science and Engineering
Physics-based machine learning, autonomous research, single-atom fabrication, scanning probe microscopy, scanning transmission electron microscopy
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Assistant Professor, Electrical Engineering and Computer Science
Computational modeling, advanced control and AI, integrated real-time robotics system
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UT–ORNL Governor’s Chair for Power Electronics
Power systems, smart grid, micro grid, infrastructure reliance, energy policy
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Assistant Professor, Electrical Engineering and Computer Science
Human-computer interaction, accessibility, ubiquitous computing, cyber-physical systems, human-robot interaction.
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Professor, Mechanical, Aerospace and Biomedical Engineering
High-performance computing, cloud computing, edge computing, reproducibility, AI-inspired workflows, AI-inspired data analytics
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Assistant Professor, Mechanical, Aerospace & Biomedical Engineering
Optimal control; convex optimization; machine learning, guidance, navigation, and control space systems; aerial vehicles; connected vehicles
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Associate Professor, Mechanical, Aerospace & Biomedical Engineering
Battery safety, thermal management, low carbon fuels, advanced combustion strategy, engine-fuel interaction