Tianming Liu

Biography

Tianming Liu is a Distinguished Research Professor and UGA Athletic Association Professor in the University of Georgia School of Computing and Department of Linguistics. He has been a full professor at UGA since 2015 and was named a Distinguished Research Professor in 2017.

He earned bachelor’s and master’s degrees in automation from Northwestern Polytechnical University and a Ph.D. in computer science from Shanghai Jiao Tong University in 2002. He completed postdoctoral training in neuroimaging at the University of Pennsylvania and Harvard Medical School, then served on the faculty at Harvard Medical School and Weill Medical College of Cornell University before joining UGA in 2008.

At UGA, Professor Liu works across computing, bioimaging, bioinformatics, neuroscience, and artificial intelligence. His group combines computational models, large-scale biomedical data, and machine learning to study the organization of the human brain and to build more capable and accountable intelligent systems.

Research program

His earlier work established computational approaches for cortical folding, structural and functional brain mapping, individualized connectivity landmarks, and large-scale functional-network analysis. These methods connect brain anatomy, connectivity, and function and support research on neurological and psychiatric disorders.

Current directions extend that foundation to brain-inspired artificial intelligence, large language models and agents, multimodal biomedical foundation models, artificial general intelligence, and quantum or hybrid quantum-classical AI. A recurring goal is to translate principles learned from human intelligence into systems that reason across modalities while remaining useful in science, medicine, education, and the humanities.

Leadership and recognition

Professor Liu received a UGA Creative Research Medal for work on human brain mapping and was named a Distinguished Research Professor in 2017. His professional service has included editorial roles for journals in neural networks, medical imaging, biomedical informatics, and computational biology, as well as serving as General Chair of MICCAI 2019. He is a Fellow of the American Institute for Medical and Biological Engineering and a recipient of NIH and NSF career awards.

Selected directions

• Large language models, agent memory, reasoning, and multimodal learning
• Brain-inspired AI and computational models of general intelligence
• Structural and functional neuroimaging, connectivity, and brain mapping
• Multimodal biomedical AI and AI for medicine
• Quantum and hybrid quantum-classical machine learning