Our research group studies how and why an organism’s traits (“phenotypes”) evolve in natural populations. Explaining the mechanisms that generate and regulate patterns of phenotypic diversity is a major goal of evolutionary biology: why do we see rapid shifts to strikingly new and distinct character states, and how stable are these evolutionary transitions across space and time? To answer these questions, we generate and analyze high-throughput “big data” on both genomes and phenotypes across the 18,000 species of reptiles and amphibians across the globe. Then, we use the statistical tools of phylogenetic comparative analysis, geometric morphometrics of 3D anatomy generated from CT scans, and genome annotation and comparative transcriptomics to understand the integrated trait correlations that create complex phenotypes. Currently, we are using machine learning and neural networks to study the color patterns of animals vouchered into biodiversity collections and test hypotheses about the ecological causes and evolutionary consequences of phenotypic innovation. We are especially passionate about the effective and accurate visualization of large-scale multidimensional datasets, and we prioritize training in both best practices and new innovations in quantitative data display.
My research focuses on using digital health solutions, signal processing, machine learning and ecological momentary assessment to understand the physiological and psychological determinants of symptoms in patients with atrial fibrillation. I am building a research framework for rich data collection using smartphone apps, medical records and wearable sensors. I believe that creating a multidimensional dataset to study atrial fibrillation will yield important insights and serve as model for studying all chronic medical conditions.
“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.
Our research is focused on Post ICU pain syndromes (PIPS). PIPS exhibit distinct phenotypic presentations and can be predicted by intra-ICU parameters. Our primary goal is to be able to predict post-ICU opioid use based on intra-ICU parameters. We utilize a data-driven characterization of post-ICU pain syndromes will utilize unsupervised clustering algorithms including DBSCAN and spectral clustering. Prediction of post-discharge pain severity, likelihood of specific pain presentations, and post-discharge opioid use will be achieved using logistic LASSO, random forests, and neural networks. Specifically, these tests will utilize available ICU data to predict changes between pre-
and post-ICU pain severity, incidence of specific pain presentations, and incidence of opioid use.
The primary tools currently in use are variations of linear models (regression, MLM, SEM, and so on) as we pursue the initial aims of the NICHD funded work. We are expanding into new areas that require new tools. Our adolescent sample is diverse, selected through quota sampling of high schools close enough to UM to afford the use of neuroimaging tools, but it is not population representative. To overcome this, we have begun work to calibrate our sample with the nationally representative Monitoring the Future study, implementing pseudo-weighting and multilevel regression and post-stratification. To enable much more powerful analyses, we are aiming toward the harmonization of multiple, high quality longitudinal databases from adolescence through early adulthood. This would benefit traditional analyses by allowing cross-validation with high power, but also provide opportunities for newer data science tools such as computational modeling and machine learning approaches.
My research focuses on the development and evaluation of novel interventions that leverage emerging technologies to train members of the healthcare workforce around adhering to guidelines. I study how to scale custom designed teaching and learning platforms and evaluate their use to motivate effective communication and dissemination of evidence based practice. Other emphases of my work include health policy literacy, translation and communication of health services research, and improving health system literacy in urban communities. I have developed and evaluated numerous web based educational interventions that employ the “flipped classroom” design with an emphasis on understanding the data and analytics that guide successful implementation and promote high fidelity for members of the healthcare workforce. As an implementation scientist, I rely on the integration of data and analytics to understand what motivates successful program implementation.
In addition to the development of these platforms, I have extensive experience developing and evaluating online, hybrid residential, residential courses, and MOOCs related to healthcare management, non-profit management, healthcare finance, and health economics that employ engaging lessons and modules, interactive graphics, and a blended learning format to aid health professions students, and both undergraduate and graduate public health students in understanding the healthcare system. My MOOC entitled “Understanding and Improving the U.S. Health Care System” has been taken by over 5,000 learners and is characterized by the use of “big data” to understand how future healthcare providers learn health policy.
I am a Research Fellow in the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan. My research is currently supported by a NSF project, Developing Evidence-based Data Sharing and Archiving Policies, where I am analyzing curation activities, automatically detecting data citations, and contributing to metrics for tracking the impact of data reuse. I hold a Ph.D. in Geography from UC Santa Barbara and I have expertise in GIScience, spatial information science, and urban planning. My interests also include the Semantic Web, innovative GIS education, and the science of science. I have experience deploying geospatial applications, designing linked data models, and developing visualizations to support data discovery.
J. Trent Alexander is the Associate Director and a Research Professor at ICPSR in the Institute for Social Research at the University of Michigan. Alexander is a historical demographer and builds social science data infrastructure. He is currently leading the Decennial Census Digitization and Linkage Project (joint with Raj Chetty and Katie Genadek) and ResearchDataGov (joint with Lynette Hoelter). Prior to coming to ICPSR in 2017, Alexander initiated the Census Longitudinal Infrastructure Project at the Census Bureau and managed the Integrated Public Use Microdata Series (IPUMS) at the University of Minnesota.
My methodological research focus on developing statistical methods for routinely collected healthcare databases such as electronic health records (EHR) and claims data. I aim to tackle the unique challenges that arise from the secondary use of real-world data for research purposes. Specifically, I develop novel causal inference methods and semiparametric efficiency theory that harness the full potential of EHR data to address comparative effectiveness and safety questions. I develop scalable and automated pipelines for curation and harmonization of EHR data across healthcare systems and coding systems.