Foundational Artificial intelligence
Closing the gap to human intelligence
Cluster Goals
The potential benefits of artificial intelligence continue to unfold for corporate, government, and institutional applications. Despite rapid advancements in deep learning, however, the gap between artificial and human intelligence is far from closed. Major funding agencies are prioritizing foundational AI research to change that.
The Foundational AI cluster at the University of Tennessee, Knoxville, is pursuing a once-in-a-generation opportunity to profoundly shape and accelerate AI development. Our vision is to build UT’s leadership position by focusing on dynamic learning in the early developmental stage of human intelligence and developing AI solutions inspired by cognitive neuroscience.
We’re deepening our foundational understanding of how human infants learn as they interact with their surroundings and applying the insights we gain to advance embodied learning in AI. We aim to apply our foundational findings to robotic learning in embodied environments and to implementation in edge devices. We envision how our work could improve applications such as surgical and social robots. Together we’ll enable AI to make life and lives better in new ways.
Intelligently Interdisciplinary
We’re bringing researchers together from fields across UT, including:
- Psychology and neuroscience
- Computer science
- Computer engineering
- Computational and applied mathematics
- Biomedical engineering
Ready to take the next step?
Why UT?
Joining UT’s Foundational AI cluster means advancing a shared vision, interfacing directly with colleagues on shared proposals and publications, and tapping into the university’s broader AI research community to expand your opportunities.
The cluster is specifically concerned with closing the gap between AI and early developmental learning. This focus distinguishes it from initiatives at other universities and concentrates our efforts. The UT Department of Psychology’s unique strengths in child development research, including six specialized labs, will undergird ongoing work across disciplines as we investigate dynamic learning from developmental, functional, mathematical, and implementation perspectives.
We’ll collaborate with the AI Tennessee Initiative, a UT-led partnership bringing AI’s benefits to manufacturing, mobility, agriculture, and other economic sectors. Former AI Tennessee Director Lynne Parker, whose wealth of experience includes leading federal AI policy efforts, will join other eminent experts in advising our cluster. UT is also investing in a science-informed AI cluster, which will enable us to cross-fertilize ideas and investigate how our insights can be translated into more applications with real-world impacts.
We will expand opportunities in AI for our students, too. We will collaborate with existing programs, such as the Bredesen Center for Interdisciplinary Research and Graduate Education Data Science and Engineering (DSE) PhD program, and build robust new curricula. Over time, new cluster hires will lead the development of interdisciplinary courses, a graduate certificate, and an undergraduate minor to prepare students for cutting-edge careers.
Join Our Academic Community
Explore the links below to learn more about open positions. Contact the faculty lead if you don’t see an open position aligned with your skill set or if you’re a current UT faculty member who wants to get involved.
Hiring Colleges
New Positions
OPEN: Apply Here
Assistant or Associate Professor
Department of Mathematics/Min H. Kao Department of Electrical Engineering and Computer Science
Focus: Theoretical machine learning
OPEN: Apply Here
Assistant or Associate Professor
Min H. Kao Department of Electrical Engineering and Computer Science
Focus: Artificial intelligence theory
OPEN: Apply Here
Assistant Professor
Min H. Kao Department of Electrical Engineering and Computer Science/Department of Mathematics
Focus: Representation learning
OPEN: Apply Here
Assistant Professor
Department of Psychology
Focus: Neurocognitive dynamics
PLANNED
Assistant Professor
Department of Mechanical, Aerospace, and Biomedical Engineering/Min H. Kao Department of Electrical Engineering and Computer Science
Focus: Reinforcement learning
PLANNED
Assistant Professor
Min H. Kao Department of Electrical Engineering and Computer Science
Focus: Machine learning accelerators
Meet Our Cluster Community
Faculty Lead
Hairong Qi
Gonzalez Family Professor, Min H. Kao Department of Electrical Engineering and Computer Science, Tickle College of Engineering
Phone: 865-974-8527
Email: hqi@utk.edu
Faculty
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Associate Professor, Psychology
executive function, cognitive development, cognitive neuroscience, computational neuroscience
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Associate Dean, Professional Licensure; Director, Graduate School of Education; Professor, Special Education
effective instructional and behavioral strategies, specifically video technologies for improving educational, functional, and social/communicative outcomes for students
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Professor, Psychology
Human development, infant perception, infant sensorimotor development, infant motor skill acquisition
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Professor & Department Head, Mathematics
numerical PDEs and scientific computing, fully nonlinear PDEs and geometric flows, systems biology and gene function prediction, nonlinear stochastic PDEs and their numerical solutions
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Professor, Civil & Environmental Engineering
system modeling and simulations, 3D visualizations, traffic engineering, application of advanced technologies to transportation, intelligent transportation systems
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James W. McConnell Professor, Electrical Engineering & Computer Science
power systems engineering and economics
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Associate Professor, Civil & Environmental Engineering
AI-enabled, human-centric, and digital twin-based cyber-physical systems; human and environmental health
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Associate Professor, Melton Faculty Fellow; Business Analytics & Statistics
big data analytics, customer analytics, data mining, healthcare analytics, machine learning
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Assistant Professor, Electrical Engineering & Computer Science
computational modeling, advanced control and AI, integrated real-time robotics system
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Assistant Professor, Electrical Engineering & Computer Science
mobile sensing and computing, cybersecurity and privacy, intelligent systems, smart healthcare, machine learning
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Professor, Mathematics; Assistant Vice Chancellor; Deputy Director of AI Tennessee Initiative
computational probability, statistics and machine learning with computational topology and geometry for addressing interdisciplinary problems in data science and engineering
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Dan Doulet Faculty Fellow, Professor, Associate Department Head, & Director of Graduate Studies; Industrial & Systems Engineering
integer programming, stochastic programming, non-linear programming, combinatorial optimization, power systems, scheduling problems, energy markets
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Associate Vice Chancellor Emerita
distributed mobile robotics, human-robot interaction, distributed intelligence, sensor networks, machine learning, embedded systems, multi-agent systems
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Professor, Electrical Engineering & Computer Science
fault-tolerance, erasure codes, storage systems, distributed computing, operating systems
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Professor & Associate Department Head, Electrical Engineering & Computer Science
nanoelectric circuit design, memristors and memristive systems, emerging nanoelectronic computer architectures, neuromorphic computing
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Joint Faculty, Electrical Engineering & Computer Science
computer vision, machine learning, natural language generation, human-computer interaction
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Assistant Professor, Electrical Engineering & Computer Science
computational imaging, biometric recognition systems, multi-modal content understanding, AI-driven precision medicine, explainability
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Assistant Professor, Computer Science
neuromorphic computing, spiking neural networks, evolutionary and genetic algorithms, machine learning on high-performance computing
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Professor, Information Sciences
gender and IT, open source software, women in STEM, social justice, online learning/communities, computer-supported cooperative work
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Professor, Electrical Engineering & Computer Science
security and privacy in wired/wireless networks and critical application systems
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Assistant Professor, Psychology
memory, attention, perception, cognitive neuroscience, electroencephalography (EEG), oscillations, machine learning
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Assistant Professor, Electrical Engineering & Computer Science
human-computer interaction, ubiquitous computing, computational materials
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Professor, Philosophy
physical, social and decisional sciences, as well as the relations amongst the sciences
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Assistant Professor, Electrical Engineering & Computer Science
model reduction of nonlinear dynamical systems, limit cycle oscillators, optimal control, neuroscientific and cardiological applications
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Associate Professor & Heath Faculty Fellow, Business Analytics & Statistics
applied statistics, big data analytics, computational statistics, data mining, healthcare analytics, predictive analytics
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Professor; Mechanical, Aerospace, & Biomedical Engineering
brain-computer interface, wearable healthcare, computational neuroscience, computational physiology, biomedical informatics