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.

I am currently a faculty lecturer at the School of Computing, University of Georgia, where I am also leading our educational and research efforts at the Applied Machine Intelligence Initiatives & Education (AMIIE) Laboratory, working with a multidisciplinary team of faculty members, students, and investigators to design, build, validate, and deploy artificial intelligence (AI) and data-driven machine learning (ML) algorithms and statistical learning in different settings, such as public health, imaging informatics, and education. I earned my BSc, MSc, and Ph.D. all in Computer Science, with a focus on AI, computer vision, and machine learning/deep learning computational components. 

I take an active interest in mentoring and teaching students, as it has been an essential part of my appointments at the University of Georgia. I have had substantial teaching experience in several undergraduate courses within the UGA School of Computing. Examples include CSCI 2610 Discrete Mathematics, CSCI 2611 Discrete Mathematics for engineering, CSCI 1301 Intro Computing Program (Java), CSCI 2725 Data Structures for data science, CSCI 2720 Data Structures and Algorithms, and CSCI 1360 Informatics and Data Analytics. 

I am the 2019 International Conference on Computational Science and Computational Intelligence CSCI Outstanding Achievement awardee, the 2021 UGA Outstanding Teaching Assistant, the NVIDIA GPU awardee, the 2020 and 2022 ACM Richard TAPIA Conference Scholarship awardee, and I were named a finalist of the 2020 NCWIT (National Center for Women and Information Technology) Collegiate Award. Of late, I have authored 25+ peer-reviewed publications, and I have organized several conferences and tutorials on computational intelligence in digital health sciences, serving as a program committee member in different conferences (e.g., ISVC).