Dr. Mezuk is the Director of the Center for Social Epidemiology and Population Health and is an Associate Chair in the Department of Epidemiology at the University of Michigan School of Public Health. She is a psychiatric epidemiologist whose research focuses on understanding the intersections of mental and physical health. Much of her work has examined the consequences of depression for medical morbidity and functioning in mid- and late-life, with particular attention to metabolic diseases such as diabetes and frailty. She is also the Director of the Michigan Integrative Well-Being and Inequalities (MIWI) Training Program, a NIH-funded methods training program that supports innovative, interdisciplinary research on the interrelationships between mental and physical health as they relate to health disparities. She is using data science tools to analyze textual data from the National Violent Death Reporting System, with the goal of better understanding how major life transitions relate to suicide risk over the lifespan. She is committed to translating research into practice, and she writes a blog for Psychology Today called “Ask an Epidemiologist.”
“Neighborhood Environments as Socio-Techno-bio Systems: Water Quality, Public Trust, and Health in Mexico City (NESTSMX)” is an NSF-funded multi-year collaborative interdisciplinary project that brings together experts in environmental engineering, anthropology, and environmental health from the University of Michigan and the Instituto Nacional de Salud Pública. The PI is Elizabeth Roberts (anthropology), and the co-PIs are Brisa N. Sánchez (biostatistics), Martha M Téllez-Rojo (public health), Branko Kerkez (environmental engineering), and Krista Rule Wigginton (civil and environmental engineering). Our overarching goal for NESTSMX is to develop methods for understanding neighborhoods as “socio-techno-bio systems” and to understand how these systems relate to people’s trust in (or distrust of) their water. In the process, we will collectively contribute to our respective fields of study while we learn how to merge efforts from different disciplinary backgrounds.
NESTSMX works with families living in Mexico City, that participate in an ongoing longitudinal birth-cohort chemical-exposure study (ELEMENT (Early Life Exposures in Mexico to ENvironmental Toxicants, U-M School of Public Health). Our research involves ethnography and environmental engineering fieldwork which we will combine with biomarker data previously gathered by ELEMENT. Our focus will be on the infrastructures and social structures that move water in and out of neighborhoods, households, and bodies.
Dr. Hadjiyski research interests include computer-aided diagnosis, artificial intelligence (AI), machine learning, predictive models, image processing and analysis, medical imaging, and control systems. His current research involves design of decision support systems for detection and diagnosis of cancer in different organs and quantitative analysis of integrated multimodality radiomics, histopathology and molecular biomarkers for treatment response monitoring using AI and machine learning techniques. He also studies the effect of the decision support systems on the physicians’ clinical performance.
My research focuses on building infrastructure for public health and health science research organizations to take advantage of cloud computing, strong software engineering practices, and MLOps (machine learning operations). By equipping biomedical research groups with tools that facilitate automation, better documentation, and portable code, we can improve the reproducibility and rigor of science while scaling up the kind of data collection and analysis possible.
Research topics include:
1. Open source software and cloud infrastructure for research,
2. Software development practices and conventions that work for academic units, like labs or research centers, and
3. The organizational factors that encourage best practices in reproducibility, data management, and transparency
The practice of science is a tug of war between competing incentives: the drive to do a lot fast, and the need to generate reproducible work. As data grows in size, code increases in complexity and the number of collaborators and institutions involved goes up, it becomes harder to preserve all the “artifacts” needed to understand and recreate your own work. Technical AND cultural solutions will be needed to keep data-centric research rigorous, shareable, and transparent to the broader scientific community.
Our team leads research on the Ethical, Legal, and Social Implications (ELSI) of learning health systems and related enterprises. Our research uses mixed methods to understand policies and practices that make data science methods (data collection and curation, AI, computable algorithms) trustworthy for patients, providers, and the public. Our work engages multiple stakeholders including providers and health systems, as well as the general public and minoritized communities on issues such as AI-enabled clinical decision support, data sharing and privacy, and consent for data use in precision oncology.
My group investigates hypertension using a principally patient-oriented approach, with key aspects of our work being collaborative with data scientists. For example, I collaborated with Casey Greene, PhD, computational biologist, on a project using generative adversarial neural networks to create a privacy-preserving dataset derivative of the SPRINT hypertension clinical trial. The work incorporated concepts from the differential privacy field, and the intent is to make clinical trial data sharing more feasible.
My research interests include health effects of air pollution, temperature extremes and climate change (mortality, asthma, hospital admissions, birth outcomes and cardiovascular endpoints); environmental exposure assessment; and socio-economic influences on health.
Data science tools and methodologies include geographic information systems and spatio-temporal analysis, epidemiologic study design and data management.
As an environmental epidemiologist and in collaboration with government and community partners, I study how social, economic, health, and built environment characteristics and/or air quality affect vulnerability to extreme heat and extreme precipitation. This research will help cities understand how to adapt to heat, heat waves, higher pollen levels, and heavy rainfall in a changing climate.
My primary research interests are understanding the causes and consequences of biased selection and attention to persuasive information, particularly in the context of health promotion. Simply stated, I am interested in what we pay attention to and why, and how this attention (or inattention) influences attitudinal and behavioral outcomes, such as persuasion and healthy behavior. In particular, my work has addressed disparities in attention to information about HIV prevention for African-Americans compared to European-Americans as a predictor of disparities in health outcomes. I am also exploring barriers to attention to health information by African-Americans, including the roles of stigma, shame, fear, and perceptions of irrelevance. At a more basic attitudes and persuasion level, I am currently pursuing work relevant to how we select information for liked versus disliked others, and how the role of choice influences how we process information we agree versus disagree with.
Kevin’s research is focused on to identifying and interpreting the mechanisms responsible for the complex dynamics we observe in ecological and epidemiological systems using data science and modeling approaches. He is primarily interested in emerging and endemic pathogens, such as SARS-CoV-2, influenza, vampire bat rabies, and a host of childhood infectious diseases such as chickenpox. He uses statistical and mechanistic models to fit, forecast, and occasionally back-cast expected disease dynamics under a host of conditions, such as vaccination or other control mechanisms.