My research focuses on understanding, designing, and evaluating learning technologies and environments that foster collaborative problem solving, spatial reasoning, engineering design thinking and agency. I am particularly interested in applying multimodal learning analytics in the context of co-located and/or virtually distributed teams in clinical simulations. I strive to utilize evidence in education science, simulation-based training and learning analytics to understand how people become expert health professionals, how they can better work in teams and how we can support these processes to foster health care delivery and health outcomes.
Shu-Fang Shih, Ph.D., has a diverse background in public health, business administration, risk management and insurance, and actuarial science. Her research has focused on design, implementation, and evaluation of theory-based health programs for children, adolescents, pregnant women, and older adults in various settings. In addition, she used econometric methods, psychometric, and other statistical methods to examine various health issues among children, adolescent, emerging adulthood, pregnant women, and the older adults. She is particularly interested in designing effective ways to align public health, social services, and healthcare to achieve the goal of family-centered and integrated/coordinated care for the family.
Jeffrey Regier received a PhD in statistics from UC Berkeley (2016) and joined the University of Michigan as an assistant professor. His research interests include graphical models, Bayesian inference, high-performance computing, deep learning, astronomy, and genomics.
I study how law shapes innovation in the life sciences, with a substantial focus on big data and artificial intelligence in medicine. I write about the intellectual property incentives and protections for data and AI algorithms, the privacy issues with wide-scale health- and health-related data collection, the medical malpractice implications of AI in medicine, and how FDA should regulate the use of medical AI.
Samuel K Handelman, Ph.D., is Research Assistant Professor in the department of Internal Medicine, Gastroenterology, of Michigan Medicine at the University of Michigan, Ann Arbor. Prof. Handelman is focused on multi-omics approaches to drive precision/personalized-therapy and to predict population-level differences in the effectiveness of interventions. He tends to favor regression-style and hierarchical-clustering approaches, partially because he has a background in both statistics and in cladistics. His scientific monomania is for compensatory mechanisms and trade-offs in evolution, but he has a principled reason to focus on translational medicine: real understanding of these mechanisms goes all the way into the clinic. Anything less that clinical translation indicates that we don’t understand what drove the genetics of human populations.
Dr. Nalliah’s research expertise is process evaluation. He has studied various healthcare processes, educational processes and healthcare economics. Dr. Nalliah’s research studies were the first time nationwide data was used to highlight emergency room resource utilization for managing dental conditions in the United States. Dr. Nalliah is internationally recognized as a pioneer in the field of nationwide hospital dataset research for dental conditions and has numerous publications in peer reviewed journals. After completing a masters degree at Harvard School of Public Health, Dr. Nalliah’s interests have expanded and he has studied various public health issues including sports injuries, poisoning, child abuse, motor vehicle accidents and surgical processes (like stem cell transplants, cardiac valve surgery and fracture reduction). National recognition of his expertise in these broader topics of medicine have given rise to opportunities to lecture to medical residents, nurse practitioners, students in medical, pharmacy and nursing programs about oral health. This is his passion- that his research should inform an evolution of health education curriculum and practice.
Dr. Nalliah’s professional mission is to improve healthcare delivery systems and he is interested in improving processes, minimizing inefficiencies, reducing healthcare bottlenecks, increasing quality, and increase task sharing which will lead to a patient-centered, coherent healthcare system. Dr. Nalliah’s research has identified systems constraints and his goal is to influence policy and planning to break those constraints and improve healthcare delivery.
My current research focus is on modeling and simulating the value and benefits of various data sharing and policy trade offs. Typically these utilize system dynamics methodologies and tools.
I also have considerable experience across multiple industries with developing processes to enable industry and faculty to identify and solve data science problems using SAS tools.