Matthew J Delano

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Obesity promotes type 2 diabetes (T2D) the 3rd leading cause of death in the United States and accounts for $237 billion healthcare cost. T2D and obesity promote infections that cause sepsis, the leading cause of mortality in the intensive care unit following traumatic injury. Despite advances in supportive care, sepsis mortality remains 25% and escalates over time. Survival from sepsis depends on emergency myelopoiesis of macrophages (MΦ) to clear bacteria and deescalate inflammation. Resolving inflammation requires MΦ polarization from pro-inflammatory M1 to anti-inflammatory MΦ states. The MΦ ability to polarize depends on the intrinsic plasticity inherited from hematopoietic stem and progenitor cells (HSPCs) during emergency myelopoiesis. Our published data in trauma and sepsis in mice and humans demonstrates that obesity and T2D alter HSPC myelopoiesis, inhibit MΦ plasticity and prevent M1Φ polarization to other functional MΦ states. However, the impact of altered MΦ myelopoiesis and restricted M1Φ polarity on sepsis pathogenesis is unknown. A critical need exists to understand the mechanisms by which obesity and T2D alter myelopoiesis, inhibit MΦ plasticity and prevent MΦ polarity to promote bacterial sepsis mortality. We hypothesize that obesity and T2D prime HSPC myelopoiesis to produce dysfunctional M1Φs incapable of bacterial clearance, and effective polarization which hinder inflammation resolution during sepsis and cause mortality. We will test the hypothesis with the following aims that, when completed, will fill the current knowledge void and improve sepsis survival. In Aim 1 we will determine the mechanisms in T2D and obesity that alter myelopoiesis in bacterial sepsis. The findings will reveal how obesity and T2D prime HSPCs and alter myelopoiesis to prevent MΦ polarity in mice and humans with sepsis. In Aim 2 we will identify the functional consequences of obese, T2D M1Φs unable to polarize to other activation states during bacterial sepsis. We will explore how restricted MΦ polarity effects immune cell responses and cytokine production to define how T2D and obesity impede inflammation resolution. The data generated will identify new pathways that promote aberrant myeloid production, restricted MΦ plasticity and prevent inflammation resolution during bacterial sepsis. Novel targeted therapies can then be developed for clinical implementation for bacterial eradication, wound healing and survival from sepsis in obesity and T2D.

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.

Halil Bisgin

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My research is focused on a wide range of topics from computational social sciences to bioinformatics where I do pattern recognition, perform data analysis, and build prediction models. At the core of my effort, there lie machine learning methods by which I have been trying to address problems related to social networks, opinion mining, biomarker discovery, pharmacovigilance, drug repositioning, security analytics, genomics, food contamination, and concussion recovery. I’m particularly interested in and eager to collaborate on cyber security aspect of social media analytics that includes but not limited to misinformation, bots, and fake news. In addition, I’m still pursuing opportunities in bioinformatics, especially about next generation sequencing analysis that can be also leveraged for phenotype predictions by using machine learning methods.

A typical pipeline for developing and evaluating a prediction models to identify malicious Android mobile apps in the market

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.

Jin Lu

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Dr. Jin Lu is an Assistant Professor of Computer and Information Science at the University of Michigan, Dearborn.
His major research interests include machine learning, data mining, optimization, matrix analysis, biomedical informatics, and health informatics. Two main directions are being pursued:
(1) Large-scale machine learning problems with data heterogeneity. Data heterogeneity is common across many high-impact application domains, ranging from recommendation system to Computer Vision, Bioinformatics and Health-informatics. Such heterogeneity can be present in a variety of forms, including (a) sample heterogeneity, where multiple resources of data samples are available as side information; (b) task heterogeneity, where multiple related learning tasks can be jointly learned to improve the overall performance; (c) view heterogeneity, where complementary information is available from various sources. My research interests focus on building efficient machine learning methods from such data heterogeneity, aiming to improve the learning model by making the best use of all data resources.
(2) Machine learning methods with provable guarantees. Machine learning has been substantially developed and has demonstrated great success in various domains. Despite its practical success, many of the applications involve solving NP-hard problems based on heuristics. It is challenging to analyze whether a heuristic scheme has any theoretical guarantee. My research interest is to employ granular data structure, e.g. sample clusters or features describing an aspect of the sample, to design new theoretically-sound models and algorithms for machine learning problems.

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.