Study Team

Rick is a Professor of Environmental Health Sciences and Global Public Health at the University of Michigan. He has published >145 manuscripts focused on assessing and controlling occupational and environmental hazards, with a particular focus on noise exposures and injury risk factors. He has served as Chair of the ACGIH® Threshold Limit Values for Physical Agents Committee, is a Fellow of the American Industrial Hygiene Association, and is a Certified Industrial Hygienist. He also directs the Center for Occupational Health and Safety Engineering and the Industrial Hygiene program at UM/ His research team created the first national-scale, quantitative job exposure matrix for noise (https://noisejem.sph.umich.edu/). 

 

Lauren is a Research Specialist in Environmental Health Sciences in the University of Michigan School of Public Health. She has more than 13 years of research experience starting in Physics, then in Orthopaedics, and finally in Public Health as lab manager for the Exposure Research Lab. She is primarily interested in occupational and environmental exposures (e.g., noise) and what risk factors (e.g., listening behaviors, hearing difficulties) contribute to disparities seen between populations. She has authored/co-authored more than 20 manuscripts, mentored countless students, and presented at various national and international conferences. She is passionate about service and environmental sustainability. She is a board member of the National Hearing Conservation Association Scholarship Foundation and Vice Chair of the Washtenaw County Board of Public Works. She has enjoyed putting to use her diverse background and skill set in her role as Project Manager for the Apple Hearing Study since it's inception.

Joyce is is an administrative coordinator for the Apple Hearing Study. She joined the School of Public Health in 2022. Joyce holds an MA from the University of Wisconsin and a BA from Western Michigan University. She has a strong background in environmental science writing and has worked for the University of Michigan since 1998 as a writer, senior editor, and administrative coordinator.

Tyler is a Research Analyst for the Apple Hearing Study at the University of Michigan School of Public Health. Tyler completed his Master of Data Science from the University of Michigan’s School of Information in 2023. Tyler has a strong background in Neuroscience and medical research, where he previously conducted Neuroscience research, specifically reward and incentive salience modeling, for five years at the University of Michigan. Tyler joined the Neitzel Lab in 2024 and has been able to integrate many data science methods and techniques he gained while completing his MS. Among the Apple Hearing Study team, Tyler has a key role in translating the massive Apple hearing datasets into meaningful insights. This includes accessing various data types, crafting code which performs sophisticated calculations on this data and then outputs it in a more manageable and digestible form. This is frequently performed through the utilization of different analytical techniques and training models with machine learning approaches. Tyler also has a supportive role in accessing and curating specific subsets of the large, overall hearing dataset. Tyler’s goals are to gain insight into the day to day noise exposures we all typically experience such that we can be more aware to ultimately have better health longevity and protection of our hearing.  

Xin is a PhD candidate in Environmental Health Sciences and Scientific Computing at the University of Michigan. She has three years of medical research experience in nursing and psychology and over four years of work in industrial hygiene, focusing on the mechanisms and risk evaluation of environmental/occupational hazards affecting community/worker health. Xin has authored and co-authored multiple manuscripts on public health impacts of environmental exposures (e.g., noise), and economic agglomeration. She has presented her work at national and international conferences, served as a peer reviewer for academic journals, and service members for professional organizations. Her current research focuses on building scalable data pipelines to enable personalized exposure modeling and health outcome prediction using real-time data from dosimeters and wearable sensors.

Abas is a doctoral student in Environmental Health Sciences. His interests lie at the intersection of industrial hygiene, environmental epidemiology, and statistics. His research examines how occupational, environmental, and social stressors contribute to environmental health disparities. Abas earned a B.S. in Statistics from the University of Michigan in 2020, followed by an M.S. in Industrial Hygiene in 2023 from the University of Michigan School of Public Health.

Jie is a doctoral student in Environmental Health Sciences and concurrently pursuing a Master's degree in Statistics at the University of Michigan. With academic foundations in nursing from Peking University and a specialization in industrial hygiene and data science from the University of Michigan, Jie brings an extensive interdisciplinary skillset to his research role on the Apple Hearing Study. Jie's primary research focuses on understanding and quantifying the health effects of occupational and environmental noise exposure. He has led national-scale epidemiological studies investigating associations between noise exposure and injury risks across diverse industries and occupations in the U.S., utilizing large datasets such as those from OSHA, MSHA and BLS. His methodological expertise includes advanced statistical techniques, Bayesian modeling and computation, and innovative knowledge-guided machine learning approaches to uncover dynamic patterns linking noise exposure to health outcomes. Beyond noise exposure research, Jie has co-authored peer-reviewed studies examining the impacts of air pollution on metabolic health and physical functioning. His research emphasizes scientific rigor, transparency, and practical implications for public health and occupational policy. Jie is passionate about integrating multidisciplinary approaches—combining exposure sciences, advanced statistics, and machine learning—to inform meaningful interventions and improve population health outcomes.