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.

Hugo Casquero

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Hugo Casquero is an Assistant Professor in the Mechanical Engineering Department at University of Michigan – Dearborn. His research is focused on developing accurate, robust, and efficient computational methods and using them to solve a myriad of open problems in fluid mechanics, solid mechanics, fluid-structure interaction, biomechanics, and multiphysics. The overarching theme of the computational methods that Dr. Casquero develops is to solve partial differential equations exploiting the new advantages that splines bring to computational mechanics. Dr. Casquero is particularly interested in developing computational frameworks for real-world applications in which experimental measurement of the quantities of interest is too costly or not currently available. Current research activities in his group include achieving a seamless integration between design and analysis of thin-walled structures, studying the dynamics of vesicles, capsules, red blood cells, and droplets under different types of flow, and developing structure-preserving spline discretizations of magnetohydrodynamics to solve problems in fusion energy.

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.

Ellen Arruda

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Mechanical behavior of materials including polymers, elastomers and soft tissue; tissue engineering of tendon and muscle constructs; constitutive modeling of growth, remodeling and functional adaptation in soft tissue; deformation mechanisms in polymers; crystal transformation mechanisms in semi-crystalline polymers; split Hopkinson pressure bar testing of polymers and elastomers for high strain rate applications including crashworthiness in automotive applications.

Rudy Richardson

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Rudy Richardson is a Professor Emeritus of Environmental Health Sciences, in the School of Public Health and of Neurology and Toxicology in Michigan Medicine. He runs the computational molecular modeling lab and is certified by the American Board of Toxicology. He works on computational/predictive toxicology including computational studies on medicinal chemistry projects focused on discovery of therapeutic agents for Alzheimer’s disease and other neurodegenerative disorders. He remains fully active in research and selective mentoring of students and postdoctoral fellows.

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.


Sara Aton

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The consolidation of recent experiences into long-term memories is a fundamental function of the brain and critical for survival. Consolidation is linked to plastic changes at synapses between neurons. However, very little is known about how this plasticity is brought about by ongoing activity in neuronal networks, and how different brain states (e.g. sleep and waking) contribute to the consolidation process.

We study how neuronal and network activity in sleeping and awake brain states contributes to plasticity following novel sensory experiences. By combining behavioral, biochemical, electrophysiological, and optogenetic techniques, we study the effects of waking experiences and sleep on neural circuits in the rodent brain.

Relationship between LFP spectral power and functional connectivity patterns in a representative mouse at baseline.

Omar Ahmed

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The Ahmed lab studies behavioral neural circuits and attempts to repair them when they go awry in neuropsychiatric disorders. Working with patients and with transgenic rodent models, we focus on how space, time and speed are encoded by the spatial navigation and memory circuits of the brain. We also focus on how these same circuits go wrong in addiction, epilepsy and traumatic brain injury.

In addition to electrophysiology in rodents and humans, we use imaging and photoactivation techniques to record and alter neuronal activity as rodents navigate custom-designed virtual reality environments. We also work on novel computational techniques to model and analyze our immensely large electrophysiology and imaging datasets to better understand how specific behaviors are encoded by neural circuits.

Dr. Ahmed received both his undergraduate and Ph.D. degrees in Neuroscience from Brown University. He then worked with epilepsy patients at Massachusetts General Hospital during his postdoctoral work, before joining the faculty at the University of Michigan as an Assistant Professor.

Polar plots showing the rhythmic phases of spikes fired by human neurons, revealing systematic variations across space and time.

David Nordsletten

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Dr. Nordsletten is an Associate Professor in the Departments of Biomedical Engineering and Cardiac Surgery. He is also a Reader in cardiovascular biomechanics at King’s College London, and is the recipient of the EPSRC HTCA leadership fellowship. His research focuses on the novel application of biomechanics integrated with magnetic resonance imaging (MRI) for the advancement of human cardiovascular health. This broad focus encompasses a range of projects spanning from numerical methods development through to direct analysis of medical imaging data for diagnostics in cardiovascular disease.

Computational biomechanical model of the biventricular heart, showing the orientation of muscle fibres within the tissue.

Robert Dick

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Robert Dick is an Associate Professor in the Department of Electrical Engineering and Computer Science, in the Electrical and Computer Engineering division. He also co-founded and served as CEO of Stryd, Inc., which produces wearable electronics for athletes. He received his Ph.D. degree from Princeton University in 2002 and his B.S. degree from Clarkson University in 1996. He worked as a Visiting Professor at Tsinghua University’s Department of Electronic Engineering in 2002, as a Visiting Researcher at NEC Labs America in 1999, and was on the faculty of Northwestern University from 2003-2008.

Prof. Dick has published in the areas of embedded operating systems, data compression, embedded system synthesis, dynamic power management, low-power and temperature-aware integrated circuit design, wireless sensor networks, human perception aware computer design, reliability, embedded system security, and behavioral synthesis. He especially likes projects in which a deep understanding of a particular application leads to a new fundamental concept or technology with broader application. He is a principal investigator in MICDE’s catalyst grant titled “Embedded Machine Learning Systems To Sense and Understand Pollinator Behavior”.

He received an NSF CAREER award and won his department’s Best Teacher of the Year award in 2004. In 2007, his technology won a Computerworld Horizon Award and his paper was selected as one of the 30 in a special collection of DATE papers appearing during the past 10 years. His 2010 work won a Best Paper Award at DATE.