Dr. Xianqiao Wang now is an Associate Professor of Mechanical Engineering and Co-Director UGA Center for Brain-inspired Artificial Intelligence. He obtained his Ph.D. degree in Mechanical Engineering in 2011 from the George Washington University, and later he joined the University of Georgia as an Assistant Professor. He has published more than 140 peer-reviewed papers in top international journals such as Advanced Materials, Science Advances, ACS Nano, Advanced Functional Materials, Brain Structure and Functions, Cerebral Cortex, Human Brain Mapping, etc. His research interests focus on data-driven brain mechanics, bio-inorganic interfaces, materials design by AI, and soft matters. His work is funded by multiple NSF and NIH grants.

Dr. Morkos' research focuses on the intersection of complex system design and manufacturing, employing AI-driven computational representation and reasoning tools. His primary investigation in engineering design delves into the fundamental "how" and "why" aspects of the design process, addressing the lack of formal computational support essential during the early stages of engineering design. In Design research, Dr. Morkos develops AI computational representation and reasoning models. These models support designers in comprehending, analyzing, synthesizing, and designing complex systems, enhancing their capabilities through AI-infused insights. In manufacturing, Dr. Morkos strives to forge formal computational bridges between design elements, such as system requirements, and computer-aided design, which significantly influences the manufacturing process. Transformer models and text/image encoders stand as instrumental tools in achieving this synergy, seamlessly translating abstract design concepts into practical manufacturing directives. The overarching objective of his research is to fundamentally reshape our comprehension and utilization of system presentations and computational reasoning capabilities. This realignment serves to facilitate the development of system models that, in turn, enable engineers and project planners to make well-informed decisions with heightened intelligence.

Dr. Jin Sun is an Assistant Professor in the School of Computing at the University of Georgia. His research interest is in developing efficient and effective deep learning and computer vision algorithms for a holistic visual understanding of complex scenes. In particular, he is interested in learning hidden information from a large collection of unlabeled visual data. He is also passionate about applying computer vision in applications to improve people’s quality of life. He has close collaboration with colleagues from areas such as agriculture, ocean science, and public health. His work has been published at top computer vision conferences such as CVPR, ICCV, and ECCV, selected as "Notable Books and Articles" in the 19th Annual ACM Best of Computing 2014, and nominated for the best paper award at CVPR 2020.

Dr. Sudhagar Mani is a Professor in the College of Engineering at the University of Georgia. He earned his Ph.D. in Chemical and Biological Engineering from the University of British Columbia, his M.Tech. in Dairy and Food Engineering from the Indian Institute of Technology, and his B.E. in Agricultural Engineering from Tamil Nadu Agricultural University.

Dr. Mani’s expertise spans biological and chemical process modeling, simulation, and optimization, with applications in sustainable biomass systems. His research includes developing biomass densification and thermal conversion technologies, conducting techno-economic and life-cycle analyses, and exploring novel methods for producing nano-cellulose, chemicals, and biocomposites. He is also interested in leveraging visualization technologies to enhance engineering education.

Dr. Hoarfrost studies the interactions between biological systems and their environment using deep learning and machine learning techniques. She also develops high-throughput experimental techniques to test hypotheses and create deep learning-scale datasets, and deploys technologies to demonstrate their performance in the field. She is particularly interested in microbial drivers of the global carbon cycle, its impact on climate and the marine ecosystem, and marine biotech. Her research topics include:

• Foundation models and transfer learning for biology
• Active learning and self-driving labs
• Deep representation learning for microbial communities and emergent phenotypes
• Characterizing uncharacterizable microbes and functions ('microbial dark matter')
• Identifying key biomarkers of the marine carbon cycle and ecosystem-level microbial phenotypes
• Modeling the biological carbon pump
• Identifying/synthesizing marine microbes for commercially relevant sustainable materials

The Hoarfrost lab is currently recruiting, we encourage interested students to reach out about opportunities.