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.
Dr. Shen is a professor in the department of computer & information science, the University of Michigan-Dearborn, USA. He is a fellow of ASME & IET, and the editor-in-chief of the International Journal of Modelling and Simulation (CiteScore 2018: 1.03), which is an EI-indexed, peer-reviewed research journal published through UK-based Taylor & Francis Group both in print and online. Professor Shen has published over 130 technical papers, 3 books, and organized many international conferences/workshops. His research areas include Modeling and Simulation, Machine Learning and Artificial Intelligence, Numerical Analysis and Optimization, Robotics, Data Science, Sensor Technology, Data Fusion, and Computational Materials Science. Dr. Shen is an innovator who is the creator of two software tools: (a) UM GeoModifier and (b) UM MatDiagnoser. He also contributed to the development of the well-known software OptiStruct.
Kamran Diba is an Associate Professor in the Department of Anesthesiology in the School of Medicine. His research group is interested in how the brain computes, coordinates, stores and transfers information. Neuronal networks generate an assortment of neuronal oscillations that vary depending on the behavior and state of an animal, from active exploration to resting and different stages of sleep and anesthesia. Accordingly, in their recordings of large populations of spiking neurons in rodents, they observe state-dependent temporal relationships at multiple timescales. What role do these unique spike patterns play and what do they tell us about the function and limitations of each brain state? To answer these and related questions, they combine behavioral studies of freely moving, learning and exploring rats, multi-channel recordings of the simultaneous electrical (spiking) activity from hundreds of neurons during behavior and sleep, neural network models of this behavior, statistical and machine learning tools to uncover deep structure within high-dimensional spike trains and chemogenetics and optogenetics to manipulate protein signaling and action potentials in specific neural populations in precise time windows.
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.
Shasha Zou is an Associate Professor of Climate and Space Science and Engineering. Her general research interest is about studying the dynamic interaction between the Sun’s extended atmosphere, i.e., solar wind, and the near-Earth space environment. In particular, she is interested in the physical processes of formation and evolution of ionospheric structures and their impact on technology, such as global navigation and communication satellite system (GNSS), during space weather disturbances using multi-instrument observations and numerical models. Numerical models often used include magnetohydrodynamic (MHD) model of the global magnetosphere, and physics-based global ionosphere and thermosphere model.
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.