Photograph of Alison Davis Rabosky

Alison Davis Rabosky

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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.

Picture of Thomas Schwarz

Thomas A. Schwarz

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Professor Schwarz is an experimental particle physicist who has performed research in astro-particle physics, collider physics, as well as in accelerator physics and RF engineering. His current research focuses on discovering new physics in high-energy collisions with the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. His particular focus is in precision measurements of properties of the Higgs Boson and searching for new associated physics using advanced AI and machine learning techniques.

Picture of Besa Xhabija

Besa Xhabija

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Dr. Xhabija joined the Department of Natural Sciences in September 2022 as an Assistant Professor of Biochemistry. Her laboratory aims to understand the effects of toxins on early embryonic development utilizing embryonic stem cells because they provide a new tool and opportunity to investigate the impact of environmental exposures and their interactions with genetic factors on human development and health. To fully realize these potentials, she believes that it is important to understand the molecular basis of the defining characteristic of the stem cells. More specifically, she is interested in investigating how stem cells play a role in shaping the expression program during development and how mechanisms of self-renewal and differentiation during mammalian development regulate cellular fate decisions.

Picture of David Brang

David Brang

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My lab studies how information from one sensory system influences processing in other sensory systems, as well as how this information is integrated in the brain. Specifically, we investigate the mechanisms underlying basic auditory, visual, and tactile interactions, synesthesia, multisensory body image perception, and visual facilitation of speech perception. Our current research examines multisensory processes using a variety of techniques including psychophysical testing and illusions, fMRI and DTI, electrophysiological measures of neural activity (both EEG and iEEG), and lesion mapping in patients with brain tumors. Our intracranial electroencephalography (iEEG/ECoG/sEEG) recordings are a unique resource that allow us to record neural activity directly from the human brain from clinically implanted electrodes in patients. These recordings are collected while patients perform the same auditory, visual, and tactile tasks that we use in our other behavioral and neuroimaging studies, but iEEG measures have millisecond temporal resolution as well as millimeter spatial precision, providing unparalleled information about the flow of neural activity in the brain. We use signal processing techniques and machine learning methods to identify how information is encoded in the brain and how it is disrupted in clinical contexts (e.g., in patients with a brain tumor).

Jordan Mckay

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Jordan McKay is a Project Associate Manager at MIDAS. An Ann Arbor native, Jordan received his Bachelors in Computer Science from University of Michigan, and his Masters in Information at the University of Michigan School of Information. Outside of business hours, Jordan also works as a conductor, concert pianist, and Music Director with a number of organizations in the Ann Arbor area.

In addition to his duties administrating the day-to-day operations for MIDAS, its website, its events, and its part-time staff, Jordan is an engaged member of the data science community. Jordan is a determined advocate for ethical AI, data sovereignty, accessibility, digital privacy, and humane information system design, and is proud to be a member of a team that is working to make data a force for good in our society.

Olga Yakusheva

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My research interests are in health economics and health services research; specifically econometric methods for causal inference, data architecture, and secondary analyses of big data. My primary focus is the study the work of nurses. I led the development of a new method for outcomes-based clinician performance productivity measurement using the electronic medical records. With this work, I was able to measure, for the first time, the value-added contributions of individual nurses to patient outcomes. This work has won her national recognition earning her the Best of AcademyHealth Research Meeting Award in 2014. I am is currently working to uncover traits and success strategies of highly-effective nurses, including education, experience, and expertise—and most recently smart clinician staffing approaches and innovation in the healthcare setting. I am a team scientist and contributed methodological expertise to many interdisciplinary projects including hospital readmissions, primary care providers, obesity, pregnancy and birth, and peer effects on health behaviors and outcomes. I am the Director of the Healthcare Innovation and Impact Program (HiiP) at the School of Nursing.

Using big data analytics to measure value-added contributions of nurses

Fabian Pfeffer

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My research investigates social inequality and its maintenance across time and generations. Current projects focus on wealth inequality and its consequences for the next generation, the institutional context of social mobility processes and educational inequality in the United States and other industrialized countries. I also help expand the social science data infrastructure and quantitative methods needed to address questions on inequality and mobility. I serve as Principal Investigator of the Wealth and Mobility (WAM) study as well as Co-Investigator of the Panel Study of Income Dynamics (PSID). As such, my research draws on and helps construct nationally representative survey data as well as full-population administrative data. My methodological work has been focused on causal inference, multiple imputation, and measurement error.

Lubomir Hadjiyski

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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.

Deena Costa

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Dr. Costa’s goal is to maximize survival and minimize morbidity for mechanically ventilated adults. She accomplishes this through her research on the organization and management of critical care. Specifically, her work identifies key structural and functional characteristics of ICU interprofessional teams that can be leveraged to improve the delivery of high quality, complex care to mechanically ventilated patients. She is a trained health services researcher with clinical expertise in adult critical care nursing. Her work care has been published in leading journals such as JAMA, Chest, and Critical Care Medicine. Her current research examines ICU teamwork and patient outcomes, linking individual clinicians to individual patients using the Electronic Health Record, and using qualitative approaches to understand how to improve ICU teams. Her research has focused on ICU clinician staffing, well-being and psychological outcomes of ICU clinicians as a way to improve care and outcomes of ICU patients.