Lana Garmire

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My research interest lies in applying data science for actionable transformation of human health from the bench to bedside. Current research focus areas include cutting edge single-cell sequencing informatics and genomics; precision medicine through integration of multi-omics data types; novel modeling and computational methods for biomarker research; public health genomics. I apply my biomedical informatics and analytical expertise to study diseases such as cancers, as well the impact of pregnancy/early life complications on later life diseases.

Carlos Aguilar

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The Aguilar group is focused understanding transcriptional and epigenetic mechanisms of skeletal muscle stem cells in diverse contexts such as regeneration after injury and aging. We focus on this area because there are little to no therapies for skeletal muscle after injury or aging. We use various types of in-vivo and in-vitro models in combination with genomic assays and high-throughput sequencing to study these molecular mechanisms.

Joshua Welch

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Our research aims to address fundamental problems in both biomedical research and computer science by developing new tools tailored to rapidly emerging single-cell omic technologies. Broadly, we seek to understand what genes define the complement of cell types and cell states within healthy tissue, how cells differentiate to their final fates, and how dysregulation of genes within specific cell types contributes to human disease. As computational method developers, we seek to both employ and advance the methods of machine learning, particularly for unsupervised analysis of high-dimensional data. We have particular expertise in manifold learning, matrix factorization, and deep learning approaches.

Nicholas Douville

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Dr. Douville is a critical care anesthesiologist with an investigative background in bioinformatics and perioperative outcomes research. He studies techniques for utilizing health care data, including genotype, to deliver personalized medicine in the perioperative period and intensive care unit. His research background has focused on ways technology can assist health care delivery to improve patient outcomes. This began designing microfluidic chips capable of recreating fluid mechanics of atelectatic alveoli and monitoring the resulting barrier breakdown real-time. His interest in bioinformatics was sparked when he observed how methodology designed for tissue engineering could be modified to the nano-scale to enable genomic analysis. Additionally, his engineering training provided the framework to apply data-driven modeling techniques, such as finite element analysis, to complex biological systems.

Jonathan Terhorst

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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.

Estimates of recent effective population sizes for various human subpopulations.

Jie Liu

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Dr. Liu’s research lab aims to develop machine learning approaches for real-world bioinformatics and medical informatics problems. We believe that computational methods are essential in order to understand many of these molecular biology problems, including the dynamics of genome conformation and nuclear organization, gene regulation, cellular networks, and the genetic basis of human diseases.

The first computational embedding method for single cells in terms of their chromatin organization.

Christopher E. Gillies

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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.

Hyun Min Kang

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Hyun Min Kang is an Associate Professor in the Department of Biostatistics. He received his Ph.D. in Computer Science from University of California, San Diego in 2009 and joined the University of Michigan faculty in the same year. Prior to his doctoral studies, he worked as a research fellow at the Genome Research Center for Diabetes and Endocrine Disease in the Seoul National University Hospital for a year and a half, after completing his Bachelors and Masters degree in Electrical Engineering at Seoul National University. His research interest lies in big data genome science. Methodologically, his primary focus is on developing statistical methods and computational tools for large-scale genetic studies. Scientifically, his research aims to understand the etiology of complex disease traits, including type 2 diabetes, bipolar disorder, cardiovascular diseases, and glomerular diseases.