Weiming Hu is an Assistant Professor in the Department of Geography at the University of Georgia and a core faculty member of the Center for Geospatial Research. His research centers on the intersection of machine learning and geoinformatics, with a particular emphasis on big spatiotemporal data analytics. He leads the Lab for Geoinformatics and AI Modeling (GAIM), which aims to advance geospatial analytics and predictive modeling by integrating artificial intelligence with remote sensing and geographic information systems (GIS).

Dr. Hu’s research is driven by a focus on quantifying and understanding uncertainty in multi-source data—from remote sensing, model simulations, and ground observations—and in hybrid dynamical–machine-learning models. His goal is to develop accurate, reliable, and trustworthy machine learning models for applications in Environmental and Earth Sciences. His work has been applied in diverse domains, including renewable energy forecasting, extreme event prediction, and water resource management, contributing to the creation of scalable, uncertainty-aware methods for studying and anticipating complex Earth system phenomena.

Wei Niu is an Assistant Professor in the School of Computing at the University of Georgia. He received his Ph.D. in Computer Science from William & Mary under the supervision of Dr. Bin Ren, following a B.S. in Software Engineering from Beihang University. Before beginning his doctoral studies, he worked as a mobile-platform software developer at Bytedance Ltd. His research focuses on real-time machine learning systems, artificial general intelligence (AGI) on embedded devices, and compiler optimization and parallel computing. His work has appeared in leading conferences and journals, including PLDI, ASPLOS, MICRO, ICCV, CVPR, AAAI, NeurIPS, ECCV, DAC, and Communications of the ACM.

In addition to his academic research, Dr. Niu has developed a number of innovative real-time AI applications for mobile devices, including projects involving network style transfer, YOLO-based object detection, super-resolution, black-and-white image and video colorization, and 3D object detection based on R2+1D, C3D, and S3D models. His recent work includes a National Science Foundation (NSF)-funded project, conducted in collaboration with Professor and School Director Gagan Agrawal, focused on enabling powerful artificial intelligence systems to run efficiently in practical computing environments.

Dr. Tao Liu is an Assistant Professor of Precision Forestry in the Warnell School of Forestry and Natural Resources at the University of Georgia. He develops and teaches courses in remote sensing, digital image processing, and GIS programming, while leading research efforts that apply deep learning and geospatial technologies to environmental monitoring and forest management. His work has addressed topics such as forest health monitoring, biomass estimation, wildfire forecasting, and crop mapping.

Before joining UGA in 2025, Dr. Liu was an Assistant Professor at Michigan Technological University and a Postdoctoral Research Associate at Oak Ridge National Laboratory. He holds a Ph.D. in Geomatics and an M.S. in Statistics from the University of Florida, an M.S. in GIS Engineering from SUNY ESF, and a B.S. in Forestry from Northwest A&F University. His research has produced influential publications in areas including remote sensing, object-based image analysis, and deep learning applications for environmental systems.

Dr. Rongjie Liu is an Assistant Professor in the Department of Statistics at the University of Georgia, where he also serves as Graduate Program Faculty within the Franklin College of Arts & Sciences. He joined UGA in 2024 after serving as an Assistant Professor at Florida State University. Dr. Liu earned his Ph.D. and M.A. in Statistics from Rice University in 2020, following a Ph.D. in Electrical and Electronic Engineering Technologies from the University of Texas at San Antonio and a B.S. in Mathematics from Southeast University in China.

His research focuses on Bayesian statistics and learning-based methods, with applications in neuroscience and engineering. Dr. Liu’s areas of expertise include Bayesian modeling, machine learning, deep and reinforcement learning, causal inference, and image data analysis. His interdisciplinary approach bridges statistical methodology and data-driven research across scientific and engineering domains.

Dr. Qianwen (Cami) Li is an Assistant Professor in the School of Environmental, Civil, Agricultural and Mechanical Engineering at the University of Georgia, where she leads the Cooperative Automated Mobility Innovations (CAMIs) Lab. Her research focuses on enhancing transportation safety and efficiency through emerging technologies, including vehicle automation, communication, electrification, and smart infrastructure.

Dr. Li earned her Ph.D. and M.S. degrees in Civil Engineering (Transportation) from the University of South Florida and a B.Eng. in Computer Science and Technology from Shandong University. Her areas of expertise include AI-powered traffic data collection and analytics, connected and automated mobility systems, transportation safety, and traffic flow theory.

Dr. Kenan Song is an Associate Professor at the University of Georgia. Before joining UGA, he was a tenured faculty member at Arizona State University (2017–2023) and completed postdoctoral research affiliated with both the Department of Materials Science and Engineering and the Department of Chemical Engineering at MIT. He received his Ph.D. in Mechanical Engineering from Northeastern University in 2014 and his B.S. in Engineering Mechanics in 2010.

Dr. Song’s research explores polymer-based nanoparticle-filled composites, with emphasis on manufacturing, characterization, simulation, and practical applications. His work seeks to elucidate the relationships between processing, structure, and properties to achieve superior structural and functional performance. Supported by funding from the National Science Foundation, Department of Defense (AFOSR & ONR), Binational Science Foundation, Qatar National Research Fund, American Chemical Society Petroleum Research Fund, Department of Health Services, and others, his research has earned him notable distinctions including the NSF CAREER Award (2022) and the ACS PMSE Young Investigator Award (2022). His expertise spans advanced manufacturing, composite engineering, polymer science, defense, energy, sustainability, and health.

Dr. Handong Yao is an Assistant Professor in the School of Environmental, Civil, Agricultural, and Mechanical Engineering (ECAM) at the University of Georgia, where he leads the Mobility Lab. His research focuses on the development of cyber-physical transportation systems driven by emerging smart infrastructure technologies, including advanced sensing, communication, and automation.

By integrating machine learning techniques and traffic flow theory, Dr. Yao’s work aims to enhance the sustainability, safety, and efficiency of modern transportation networks. His areas of expertise include cyber-physical transportation systems, traffic flow theory and modeling, AI-enhanced safety, and electric mobility.

Chao Huang is an Assistant Professor in the Department of Epidemiology & Biostatistics at the University of Georgia. He earned his Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill in 2019 and his B.S. in Applied Mathematics from Southeast University in 2008. His research centers on statistical learning for large-scale biomedical data, encompassing clinical, imaging, and genomic datasets.

Dr. Huang develops novel statistical methods and machine learning algorithms—including deep learning approaches—for analyzing complex and heterogeneous data structures such as high-dimensional, functional, and manifold data. His work contributes to advancing understanding of disease progression and improving the design of clinical trials for treatment and early prevention. Current projects in his group involve big data integration, manifold and functional data analysis, imaging heterogeneity, imaging genetics, causal inference, and deep learning.

Dr. Cameron D. Thomas, PharmD, PhD, is an Assistant Professor in the Department of Clinical & Administrative Pharmacy at the University of Georgia and a Faculty Fellow in the AI Department. He earned his PharmD from the University of Florida, followed by a PGY-1 Clinical Pharmacy residency at UF Health Jacksonville and a PGY-2 Clinical Pharmacogenetics residency at St. Jude Children’s Research Hospital. He later completed his PhD at the University of Florida, focusing on precision medicine approaches to antiplatelet therapy following percutaneous coronary intervention.

Dr. Thomas’ research integrates pharmacogenomics, clinical risk stratification, and artificial intelligence to improve patient outcomes across multiple therapeutic areas. His work applies machine learning and natural language processing (NLP) to analyze electronic health records, extract and annotate clinical events, and develop predictive models for adverse drug outcomes. In cardiovascular medicine, he has advanced CYP2C19-guided P2Y12 inhibitor selection and clinical-genetic risk tools to optimize dual antiplatelet therapy. Overall, his research seeks to personalize pharmacotherapy through data-driven methods that balance efficacy and safety in patient care.

Dr. Beiwen Li is an Associate Professor of Mechanical Engineering at the University of Georgia and now also a Faculty Fellow with the AI Department. Before joining UGA, he served seven years at Iowa State University, where he was an Assistant and Associate Professor and held the William and Virginia Binger Endowed Professorship. His research specializes in high-speed three-dimensional optical sensing, particularly as it applies to manufacturing and remanufacturing processes. His work has been prominently featured on the covers of leading journals including Optics Express and Applied Optics.

Dr. Li’s research program has been supported by a wide range of sponsors, including the National Science Foundation, the REMADE Institute, the Department of Energy, the U.S. Army Engineer Research and Development Center, the Iowa Energy Center, FEMA, and several industry partners. His contributions have been recognized with multiple national and institutional honors, among them the 2020 SPIE Rising Researcher Award, the 2021 Emerging Leaders in Measurement Science and Technology Award, the 2023 Iowa State University College of Engineering Early Achievement in Research Award, and the 2024 Susan Smyth SME Outstanding Young Manufacturing Engineer Award.