People
Faculty
Debarshi Datta
Debarshi Datta, PhD, is an Assistant Professor in Data Science at the Christine E. Lynn College of Nursing, Florida Atlantic University. He completed a PhD in Experimental Psychology at the Charles E. Schmidt College of Science, Florida Atlantic University. Dr. Datta has experience developing AI-driven decision-support systems for healthcare data, including understanding problem statements, handling disputes, conducting exploratory data analysis, building models, and data visualization and storytelling. Dr. Datta’s current research focuses on data-driven domains, such as AI/ML, to understand population-based disease prognosis. His primary research contribution has been to determine the severity of the disease, inform decision-making for developing a model that comprehends the most significant features predicting mortality and disease severity, and to utilize traditional AI/ML techniques such as decision trees, random forests, XGBoost, and deep learning. In other research, he is developing a model to predict dementia early. Dr. Datta has received numerous intramural grants, including Early Prediction of Alzheimer's Disease and Related Dementias on Preclinical Assessment Data using Machine Learning tools; Seed Funding from Smart Health for COVID-19 research; NSF I-Corps Customer Discovery Funding; and the ALL of US Institutional Champion Award, among many others.
Morgan Cooley
Morgan Cooley earned her Ph.D. in Marriage and Family Therapy in 2014 and MSW in Social Work in 2009 from Florida State University in Tallahassee, Florida. She taught as an Assistant Professor between 2014-2018 at the University of Tennessee at Chattanooga. Currently, Dr. Cooley is a social work faculty member at Florida Atlantic University. She is a licensed clinical social worker with practice experience in couple and family therapy, working with families impacted by the child welfare system, and mental health and trauma. Dr. Cooley’s research is greatly influenced by a background in both social work and family science and focuses on examining the relationships between child mental health, family systems, and the child welfare system. Specifically, she is interested in the relationship quality between children and foster caregivers, foster family well-being, foster parent preservice training, supporting relationships between birth and foster families for the benefit of youth in care, factors associated with improved mental health of youth in care, child welfare policy, and training for foster caregivers and child welfare professionals..
David Newman
David Newman, PhD, is a Professor and Biostatistician at Christine E. Lynn College of Nursing, Florida Atlantic University. He is also one of the seven faculty members assigned to the Biostatistician Core and helps to support research across the university. Dr. Newman has more than 100 scholarly publications, teaches advanced statistics and research methodology classes in the Ph.D. program with a focus on longitudinal data analysis using generalized linear mixed models, latent growth models, and machine learning and AI models. Dr. Newman is the principal investigator or co-investigator on multiple NIH and other federal, state, and foundation grants. His research aligns with two of FAU's research pillars, the Institute for Human Health and Disease Intervention (I-HEALTH) and the Institute for Sensing and Embedded Network Systems Engineering (I-SENSE). Dr. Newman’s CON’s research focuses on areas of aging across the lifespan, with a concentration on geriatrics, cognitive functioning, physical functioning, and pain management. The partnership between I-HEALTH and I-SENSE has enabled Dr. Newman and his co-researchers to obtain more precise continuous measurements of key variables, thereby enhancing the accuracy and stability of the models.
Alan Kunz-Lomelin
I’m Dr. Alan Kunz-Lomelin, Assistant Professor in the School of Social Work at Florida Atlantic University and a Licensed Clinical Social Worker (LCSW). My work focuses on the next era of behavioral health and human services, where artificial intelligence and large language models become part of the infrastructure of care, education, and research. I examine how these tools can strengthen human judgment rather than replace it by augmenting clinical workflows, expanding access to high-quality information, supporting training and supervision, and accelerating research in mental health and substance use systems, while also addressing the governance, workforce, and accountability challenges that emerge at scale. Methodologically, I draw on quantitative approaches, applied AI, and social network analysis to connect innovation with measurable outcomes and system readiness.
