I conduct research on the use of consumer-facing technologies for chronic disease self management. My work predominantly centers on the use of mobile applications that collect and manage patient generated health data overt time.
Dr. Aaronson is actively engaged in clinical practice and clinical research in the areas of heart failure, heart transplantation and mechanical circulatory support. His research has focused on improving health, quality of life and economic outcomes in these populations, utilizing methodologies ranging from small to large scale observational analyses, Markov modeling, meta-analyses, and both industry-sponsored and investigator-initiated randomized clinical trials of standard pharmaceutical interventions, alternative medicines, patient education and mechanical circulatory support. Dr. Aaronson has had a particular interest in modeling outcomes in advanced heart failure, heart transplantation and mechanical circulatory support to inform appropriate utilization of health resources.
My research involves developing novel data collection strategies and image reconstruction techniques for Magnetic Resonance Imaging. In order to accelerate data collection, we take advantage of features of MRI data, including sparsity, spatiotemporal correlations, and adherence to underlying physics; each of these properties can be leveraged to reduce the amount of data required to generate an image and thus speed up imaging time. We also seek to understand what image information is essential for radiologists in order to optimize MRI data collection and personalize the imaging protocol for each patient. We deploy machine learning algorithms and optimization techniques in each of these projects. In some of our work, we can generate the data that we need to train and test our algorithms using numerical simulations. In other portions, we seek to utilize clinical images, prospectively collected MRI data, or MRI protocol information in order to refine our techniques.
We seek to develop technologies like cardiac Magnetic Resonance Fingerprinting (cMRF), which can be used to efficiently collect multiple forms of information to distinguish healthy and diseased tissue using MRI. By using rapid methods like cMRF, quantitative data describing disease processes can be gathered quickly, enabling more and sicker patients can be assessed via MRI. These data, collected from many patients over time, can also be used to further refine MRI technologies for the assessment of specific diseases in a tailored, patient-specific manner.
Age- and sensory-related deficits in balance function drastically impact quality of life and present long-term care challenges. Successful fall prevention programs include balance exercise regimes, designed to recover, retrain, or develop new sensorimotor strategies to facilitate functional mobility. Effective balance-training programs require frequent visits to the clinic and/or the supervision of a physical therapist; however, one-on-one guided training with a physical therapist is not scalable for long-term balance training preventative and therapeutic programs. To enable preventative and therapeutic at-home balance training, we aim to develop models for automatically 1) evaluating balance and, 2) delivering personalized training guidance for community dwelling OA and people with sensory disabilities.
Shobita Parthasarathy studies the governance of emerging science and technology as well as the politics of evidence and expertise in policymaking, in comparative and international perspective. She has a long-standing interest in the use and regulation of genomic and genetic data. Her first two books, Building Genetic Medicine: Breast Cancer, Technology, and the Comparative Politics of Health Care (MIT Press, 2007) and Patent Politics: Life Forms, Markets, and the Public Interest in the United States and Europe, (University of Chicago Press, 2017) cover these themes. Using comparative and qualitative interpretive research methods, she studies the the ethics, politics, and economics of data collection and interpretation. This includes concerns about consent and intellectual property in genomic databases, the social implications of commodifying data, the use of personal data in determining access to social services and health care, and the use of data for social justice and public good.
Her current research focuses on the politics of inclusive innovation in international development, with a focus in India. She is interested in how political culture and ideology shape what counts as inclusive “innovation”, and in the implications for social and political order—particularly the “empowerment” of poor girls and women.
I develop probabilistic and statistical models to analyze genetic and genomic data. We use these methods to study evolution, natural selection, and human history. Recently, I have been interested in applying these techniques to study viral epidemics (e.g., HIV) and cancer.
I am Research Faculty with the Michigan Center for Integrative Research in Critical Care (MCIRCC). Our team builds predictive algorithms, analyzes signals, and implements statistical models to advance Critical Care Medicine. We use electronic healthcare record data to build predictive algorithms. One example of this is Predicting Intensive Care Transfers and other Unforeseen Events (PICTURE), which uses commonly collected vital signs and labs to predict patient deterioration on the general hospital floor. Additionally, our team collects waveforms from the University Hospital, and we store this data utilizing Amazon Web Services. We use these signals to build predictive algorithms to advance precision medicine. Our flagship algorithm called Analytic for Hemodynamic Instability (AHI), predicts patient deterioration using a single-lead electrocardiogram signal. We use Bayesian methods to analyze metabolomic biomarker data from blood and exhaled breath to understand Sepsis and Acute Respiratory Distress Syndrome. I also have an interest in statistical genetics.
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.
The long temporal and large spatial scales of ecological systems make controlled experimentation difficult and the amassing of informative data challenging and expensive. The resulting sparsity and noise are major impediments to scientific progress in ecology, which therefore depends on efficient use of data. In this context, it has in recent years been recognized that the onetime playthings of theoretical ecologists, mathematical models of ecological processes, are no longer exclusively the stuff of thought experiments, but have great utility in the context of causal inference. Specifically, because they embody scientific questions about ecological processes in sharpest form—making precise, quantitative, testable predictions—the rigorous confrontation of process-based models with data accelerates the development of ecological understanding. This is the central premise of my research program and the common thread of the work that goes on in my laboratory.