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 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.
Today’s real-world problems are complex and large, often with an overwhelmingly large number of unknown variables which render them doomed to the so-called “curse of dimensionality”. For instance, in machine learning, it is important to obtain simple, interpretable, and parsimonious models of high-dimensional and noisy data. As another example, in large-scale dynamic systems, a long standing question is how to efficiently design distributed control policies for unknown and interconnected systems.
Our research is centered around two main objectives: (1) to model these problems as tractable optimization problems; and (2) to develop structure-aware and scalable computational methods for solving these optimization problems that come equipped with certifiable optimality guarantees. At the core of our research is to show that exploiting hidden structures in these problems—such as graph-induced or spectral sparsity—is a key game-changer in the pursuit of massively scalable and guaranteed computational methods.
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 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.
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.
Yin Lu (Julie) Young is a Professor in the department of Naval Architecture and Marine Engineering. Her research focuses on the dynamic fluid-structure interaction response and stability of smart/adaptive multi-functional marine structures such as marine propulsors, turbines and control surfaces. One of her research focus is the fluid-structure interaction response and stability of marine and coastal structures. She is the current director of the Aaron Friedman Marine Hydrodynamics Laboratory. Her research has been supported by the Office of Naval Research (ONR), the Naval Surface Warfare Center (NSWC), and the National Science Foundation (NSF).
Mark Guzdial is a Professor of Computer Science & Engineering, and in the School of Information. His focus is on engineering education research, specifically computing education. He studies how people come to understanding computing, and how that understanding can be facilitated.
Mark Allison is an Assistant Professor of Computer Science at the University of Michigan Flint Campus. His primary area of research is model-driven engineering targeting complex software systems. Domains under study are autonomous and autonomic cyber-physical systems. Currently he is exploring Autonomous Underwater vehicles in swarms. Prof. Allison’s secondary research area relates to Computer Science pedagogy.