Summer Institute in Computational Social Science

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

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David Newman

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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.

Subhosit Ray

Subhosit Ray, PhD, is a Senior Research Fellow in Data Science at the Christine E. Lynn College of Nursing, Florida Atlantic University. His research applies computational methods and machine learning to large-scale social and health datasets, including geospatial and GPS-derived behavioral data and population-scale electronic health records. His work focuses on identifying patterns of human behavior and health outcomes using data-driven approaches, with applications in detecting cognitive change, modeling disease risk, and understanding population-level health dynamics. Dr. Ray’s recent research includes developing explainable machine learning models using large-scale research platforms such as the All of Us Research Workbench to identify early predictors of Alzheimer’s disease and related dementias. He has also contributed to predictive modeling and interpretable machine learning approaches to study disease severity in infectious diseases such as COVID-19. Dr. Ray completed his PhD in the Department of Psychology at Florida Atlantic University, where his doctoral research focused on developing self-organizing deep learning architectures for reinforcement learning using graph cellular automata. In addition to his work in healthcare data science, he has experience in biomedical signal processing, including analyzing EEG signals to study the frequency dynamics of visual attention and developing computational approaches to analyze ECG data for identifying sources of atrial fibrillation. Dr. Ray has contributed to several funded research initiatives, including projects supported by the All of Us Research Program and pilot funding programs focused on Alzheimer’s disease and related dementias. At the Summer Institute in Computational Social Science (SICSS), he serves as an instructor and mentor, supporting participants in applying computational and machine learning methods to interdisciplinary social science research.