Carlos Aguilar

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The schematic is a series of muscle images during the regenerative process, whereby resident stem cells repair the tissue.

Skeletal muscle contains multiple cell types, regenerates when damaged via a pool of resident stem cells (called satellite cells), consumes significant amounts of metabolic energy, grows and adapts its structure and function based on its environment. Nearly all actions in life are defined by the coordinated actions of skeletal muscle but when afflicted by injury or aging, muscle function decreases and quality of life is reduced. Currently, there are little to no therapies for recovery after severe trauma or age-associated muscle wasting (sarcopenia). Prof. Aguilar’s laboratory focuses on studying molecular mechanisms satellite cells use after trauma and aging with a particular focus on transcriptional and epigenetic regulation. We develop and utilize different types of high-throughput sequencing based assays and sophisticated bioinformatics algorithms to generate these insights.

Albert S. Berahas

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Albert S. Berahas is an Assistant Professor of Industrial & Operations Engineering. Dr. Berahas received a BSC in Operations Research and Industrial Engineering from Cornell University, and his MSC and Ph.D.degrees in Engineering Sciences and Applied Mathematics from Northwestern University. Before coming to the University of Michigan, Dr. Berahas held positions as a Postdoctoral Research Fellow at Lehigh University and at Northwestern University.

Dr. Berahas’ research broadly focuses on designing, developing and analyzing algorithms for solving large scale nonlinear optimization problems. Such problems are ubiquitous, and arise in a plethora of areas such as engineering design, economics, transportation, robotics, machine learning and statistics. Specifically, he is interested in and has explored several sub-fields of nonlinear optimization such as: (i) general nonlinear optimization algorithms, (ii) optimization algorithms for machine learning, (iii) constrained optimization, (iv) stochastic optimization, (v) derivative-free optimization, and (vi) distributed optimization.

Joshua Stein

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Josua Stein is an associate professor of Ophthalmology and Visual Sciences at Michigan Medicine. He is the principal investigator of the Sight Outcomes Research Collaborative (SOURCE) consortium, a group of academic ophthalmology departments who are extracting EHR data and data from ocular diagnostic tests for all eye care recipients at their respective institutions, removing all PHI, and sending their data to the University of Michigan where him and his team are cleaning and aggregating the data and making it available to researchers at the various sites for research and Q/I projects. The team is integrating data from SOURCE into machine learning algorithms, applying systems engineering techniques to forecast disease trajectory for chronic eye diseases such as glaucoma, and other precision medicine initiatives.

Andrew Brouwer

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Andrew Brouwer is an Assistant Research Scientist in the Department of Epidemiology at the University of Michigan. He earned his PhD in applied and interdisciplinary mathematics (2015) at the University of Michigan; he also has an MA in statistics and an MS in environmental science and engineering. Andrew is a mathematical epidemiologist whose research focuses on mathematical and statistical modeling for public health, particularly models of infectious disease and cancer. Rigorous consideration of parameter identifiability, parameter estimation, and uncertainty quantification are underlying themes in Andrew’s work.

Arvind Rao

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Arvind Rao is an Associate Professor in the Department of Computational Medicine and Bioinformatics, and Radiation Oncology in the School of Medicine. 

His research is in:

1. Transcriptional Genomics: A bioinformatics framework that identifies tissue‐specific enhancers by integrating multi‐modal genomic data has been developed previously [Rao2010]. There is interest to integrate other sources of information (like epigenomic and ChIP datasets) to improve the efficacy of enhancer prediction. We have also participated in the TCGA Glioma groups’ work [Brat2015, Ceccarelli2016] on identifying transcriptional regulators underlying gliomagenesis.

2. Image Informatics: In order to quantify the phenotypic aspects of disease and their relationships with outcome and their genetic context, we have developed methods for the analysis of histopathology [ Mousavi2015, Vu2016] and radiology [Yang2015] images, focusing on tumor heterogeneity. One direction of our group is to develop image analysis tools to delineate tumor image features from radiology data and to develop predictive models to relate them along with underlying genomic measurements to outcomes in low grade gliomas. Further, we have also investigated methodologies to link tumor imaging, genetics and immune status in gliomas. More recently, my group has been studying the relationship between image-derived features, genetics and cognitive status in glioblastoma patients. Further, we have also developed methods for the analysis of multiparametric MR datasets in Radiation Oncology.

3. Heterogeneous Data Integration: Integrative decision making in the clinical domain involves the need for principled formalisms that can integrate pathology, imaging and genomic data sets to drive hypothesis generation and clinical action. We have focused on developing high throughput measurement pipelines from this diverse array of data sources and methods for their integration. Simultaneously, methods for visualization are also under investigation. A more recent interest of our group is to integrate genomics, imaging and (online) behavioral data from patient to assess their evolving response to treatment, in the context of learning healthcare platforms. This could also enable the development of hybrid diagnostics.

4. Informatics for Combinatorial Drug Screens: the availability of multimodal data sources (cell line genomics, drug assays) coupled with high throughput, high content imaging platforms have created the need for informatics frameworks to identify rational drug combinations capable of modulating disease-associated phenotype. In this context, we have worked with the Gulf Coast Consortium to create analysis platforms that jointly mine imaging and genomics data for combinatorial drug discovery.

 

The overall goal is to link different data sources, such as imaging-derived phenotypes with genomic alteration for clinical predictive models. This has prompted work in AI/ML models for image processing &computer vision, data integration and genomic analysis.

 

Camille Avestruz

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Dr. Avestruz is a computational cosmologist. She uses simulations to model, predict, and interpret observed large-scale cosmic structures. Her primary focus is to understand the evolution of galaxy clusters. These are the most massive gravitationally collapsed structures in our universe, comprised of hundreds to thousands of galaxies. Other aspects of her work prepare for the next decade of observations, which will produce unprecedented volumes of data. In particular, she is leading software development efforts within the clusters working group of the Large Synoptic Survey Telescope to calibrate galaxy cluster masses from simulation data. Dr. Avestruz also incorporates big data methods, including machine learning, to extract gravitational lensing signatures that probe the mass distribution of massive galaxies and galaxy clusters.

[Click on image to see video] Image projection of various components and properties of a simulated galaxy cluster in its last 8 gigayears of formation. The top left panel shows the underlying dark matter content, the top middle image shows the distribution of stars, and the remaining four panels are properties of the gas content: density, temperature, entropy, and metallicity. To model the evolution of galaxy clusters in a cosmological volume, the simulation uses adaptive refinement in space and time in order to span the relevant dynamic range of the system.

Fernanda Valdovinos

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Fernanda Valdovinos is an Assistant Professor in the department of Ecology and Evolutionary Biology and Complex Systems. She received her Ph.D. in Ecology and Evolutionary Biology from the Faculty of Science, University of Chile in 2008. Before joining the University of Michigan, she was a researcher in the Estación Biológica de Doñana, Spain, at the Pacific Ecoinformatics and Computational Lab in Berkeley, CA and at the department of Ecology and Evolutionary Biology at the University of Arizona.

Her lab studies the structure and dynamics of ecological networks at ecological and evolutionary scales; including their resilience to biodiversity loss, biological invasions, climate change, and exploitation by humans. She is a principal investigator in MICDE Catalyst Grant: “Embedded Machine Learning Systems To Sense and Understand Pollinator Behavior”.

Jianzhi (George) Zhang

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Jianzhi (George) Zhang is a Professor of Ecology and Evolutionary Biology interested in the relative roles of chance and necessity in evolution. He got his B. S. from Fudan University in Shanghai, China, and his Ph. D. in Genetics from Pennsylvania State University. He was a  Fogarty postdoctoral fellow at the National Institute of Allergy and Infectious Diseases before moving to the University of Michigan.

Professor Zhang’s research focuses on two main research areas:  (1) yeast as an experimental system for studying evolution, where his research group uses the budding yeast Saccharomyces cerevisiae and its relatives as model organisms to understand a variety of evolutionary processes such as the genetic basis of phenotypic variations among strains and species, or molecular and genomic bases of heterosis; and (2) computational evolutionary genomics where they use evolutionary, genomic, and/or systemic approaches to analyze publicly available data to characterize and understand pleiotropy, robustness, epistasis, gene-environment interaction, gene expression noise, translational regulation, RNA editing, convergent evolution, adaptation, origin of new genes, among-protein evolutionary rate variation, and other important genetic and evolutionary phenomena. Projects may also involve modeling and simulation, including the MICDE catalyst grant project where the team is using deep neural networks to infer molecular phylogenies and extract phylogenetically useful patterns from amino acid or nucleotide sequences, which will help understand evolutionary mechanisms and build evolutionary models for a variety of analyses.

Xun Huan

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Xun (Ryan) Huan is an Assistant Professor in the Department of Mechanical Engineering. His research broadly revolves around uncertainty quantification, data-driven modeling, and numerical optimization. His expertise focuses on bridging models and data: optimal experimental design, Bayesian methods for statistical inference, uncertainty propagation in high-dimensional settings, and methods that are robust to model misspecification. He seeks to develop efficient computational methods that integrate realistic models with big data, and combine uncertainty quantification with machine learning to enable robust prediction, design, and decision-making. He is interested in collaborative opportunities in various applications that can benefit from a better understanding of uncertainty and modeling. Current research activities include assessing uncertainty in deep neural networks, and developing sequential experimental design methods for improving autonomy.

Optimal experimental design seeks to identify experiments that produce the most valuable data, and can lead to substantial resource savings. For example, in the design of a shock-tube combustion experiment, design condition A maximizes the expected information gain. When Bayesian inference is performed on data from this experiment, we obtain “tighter” posteriors (with less uncertainty) compared to those obtained from suboptimal design conditions B and C.

Raed Al Kontar

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Rael Al Kontar is an Assistant Professor in the department of Industrial & Operations Engineering. His research broadly focuses on developing data analytics and decision-making methodologies specifically tailored for Internet of Things (IoT) enabled smart and connected products/systems. He envisions that most (if not all) engineering systems will eventually become connected systems in the future. Therefore, his key focus is on developing next-generation data analytics, machine learning, individualized informatics and graphical and network modeling tools to truly realize the competitive advantages that are promised by smart and connected products/systems.

Towards smart and connected systems