Dr. Gerald C. (Jerry) Kane is a Professor and the C. Herman and Mary Virginia Terry Chair in Business Administration at the Terry College of Business at the University of Georgia. His research interests include the social and ethical implications of artificial intelligence and machine learning; how companies use digital tools to innovate through disruption (e.g. COVID-19); the success factors associated with the digital transformation of legacy companies; using social media to manage knowledge within, between, and across firm boundaries; and the intersection of information systems and social networks, particularly in healthcare organizations.  He speaks about how companies can understand and respond to digital disruption to undergraduate, graduate, and executive education students worldwide.  He has published over 100 papers, articles, and reports on these topics in journals such as MIS Quarterly, Information Systems Research, Organization Science, Management Science, Marketing Science, Journal of Management Information Systems, Journal of the AIS, Journal of Business Ethics, Harvard Business Review, MIT-Sloan Management Review, among others. He is the lead author of The Technology Fallacy: How People are the Real Key to Digital Transformation, and The Transformation Myth: Leading Your Organization Through Uncertain Times, both published by MIT Press.  He is formerly a Senior Editor at MIS Quarterly.

Katherine Ireland is the Assistant Director of the M. Douglas & V. Kay Ivester Institute for Business Analytics and Insights at the Terry College of Business, University of Georgia. Her scholarship sits at the intersection of data science, linguistics, and computational methods, and she is a faculty fellow in UGA’s Institute for Artificial Intelligence. She recently completed a Ph.D. in Linguistics at the University of Georgia, specializing in corpus linguistics and computational humanities, and has published in venues such as Applied Corpus Linguistics, BYU Law Review, and the Journal of English Linguistics, with additional articles forthcoming in Cambridge’s Data Intensive Investigations of English.

Ireland’s professional experience includes serving as Interim Head of the UGA Libraries’ DigiLab, as well as research assistant roles with the DigiLab and the Linguistic Atlas Project. She has also contributed to teaching as a TA for John Hale’s Text and Corpus LING/ENGL 4/6{886} course. Her work is highly interdisciplinary, drawing on linguistics, data science, and technology to address questions across business and society.

Dr. Edwards’s research focuses on the physical oceanography of the continental margins, where shelf-scale processes can have complicated interactions with topography and stratification at the nearshore boundary as well as the shelfbreak. Her current work takes a joint observational/modeling approach to describing the response of the coastal ocean to near-resonant forcing by sea breeze and land breeze near the critical latitude for diurnal/inertial resonance. Heating and cooling of shelf water also induce significant diurnal and supertidal variability in the coastal ocean, but the importance of air-sea interaction and subsynoptic meteorological variability is often neglected for circulation and ecosystem modeling on a regional scale. This higher frequency variability in the ocean and atmosphere (and associated mixing) has important implications for larval transport, nutrient budgets, and the larger coastal ecosystem.

Dr. Zhen Xiang on trustworthy machine learning, large foundation models, and AI agents. His recent research primarily focuses on AI agents powered by large foundation models, including:

• The deployment of AI agents in healthcare, autonomy, education, and scientific tasks.
• The safety and security of AI agents in high-stakes applications.
• The creation of guardrail agents tackling safety, privacy, and fairness issues within AI applications.

Dr. Ye Shen is the Ernest Corn Professor and Head of the Department of Epidemiology and Biostatistics in the College of Public Health at the University of Georgia. He is also affiliated with the Institute of Gerontology. His expertise lies in statistical methods for epidemiologic research, including longitudinal data analysis, spatial statistics, and causal inference.

Dr. Shen’s research interests include joint modeling of longitudinal and recurrent event data, survival analysis, and semi-parametric regression methods. He also investigates missing data problems in clinical trials and develops statistical approaches for complex biomedical and public health studies. His work integrates methodological rigor with applications in epidemiology and biostatistics.

Dr. Lu's research primarily highlights three academic passions: (1) designing and implementing AI-driven immersive technologies in healthcare professions education, (2) measuring the learning experience through a multidimensional lens, and (3) working with K-12 educators on technology adoption and integration with a focus on GAI in education. Methodologically, her work often employs design-based research and mixed-methods approaches.

Dr. Andrew Whitford is the Crenshaw Professor of Public Policy in the School of Public and International Affairs at the University of Georgia. His research centers on sociotechnical systems, with particular attention to the intersection of governance, technology, and information policy. He holds a Ph.D. in Political Science from Washington University in St. Louis.

His current projects address topics such as the governance of complex data resources, the diffusion of knowledge commons, and the role of legal identity in technology. Dr. Whitford also studies issues of moral hazard, monitoring, and measurement across historical and contemporary contexts. His areas of expertise include strategy and innovation, information policy, and the study of emerging technologies.

Weifeng Li is an Associate Professor in the Department of Management Information Systems at the University of Georgia. He earned his Ph.D. in Management Information Systems from the University of Arizona. Dr. Li’s research focuses on the security of artificial intelligence systems and the development of AI technologies for cybersecurity applications. His methodological expertise spans machine learning, natural language processing, and Bayesian modeling.

His work has been published in premier academic journals and conferences, including MIS Quarterly, Journal of Management Information Systems, IEEE Transactions on Knowledge and Data Engineering, and ACM Computing Surveys. His research has been supported by the National Science Foundation’s Secure and Trustworthy Cyberspace (SaTC) program. Through his scholarship, Dr. Li advances understanding of how AI can be both safeguarded and leveraged to enhance digital security in complex and evolving information environments.

My research focuses on improving quantitative tools for modeling forest growth and yield in pure and mixed-species forests to inform management decisions. I am particularly interested in investigating the advantages and limitations of both existing and alternative statistical methods (e.g., machine learning, survival analysis, small area estimation) to address management challenges in forestry practice.

My lab at the Warnell School of Forestry and Natural Resources is seeking one to two graduate students. The research will focus on developing small area estimation (SAE) methods to improve forest inventory and growth projection for southern pine plantations. This collaborative project involves various forest industry companies and state forests, providing an excellent opportunity to gain experience in applying statistical and computational methods to solve practical forestry questions. Research topics include, but are not limited to:

1. Investigating the applications of unit-level models, both with and without random effects, for predicting tree lists and/or diameter distributions.
2. Examining the impact of varying levels of ground GPS spatial precision on SAE estimates.
3. Evaluating alternative quantitative methods (e.g., machine learning) within the SAE framework.
4. Assessing the precision of total volume estimates using SAE-derived input variables in commonly used growth and yield systems.
5. Exploring the incorporation of previous inventories and/or historical remote sensing data as auxiliary information in growth projections.

The selected students will be responsible for presenting research results at Plantation Management Research Cooperative (PMRC) meetings and professional conferences, as well as publishing relevant work in peer-reviewed journals. Priority will be given to applicants with backgrounds in statistics, computer science, and GIS. Funding is available (e.g., tuition, stipend, travel fund) if needed.

Please send your CV and a brief description of your experience relevant to this research topic to syang23@uga.edu.