Kathryn Luker

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As an expert in molecular imaging of single cell signaling in cancer, I develop integrated systems of molecular, cellular, optical, and custom image processing tools to extract rich data sets for biochemical and behavioral functions in living cells over minutes to days. Data sets composed of thousands to millions of cells enable us to develop predictive models of cellular function through a variety of computational approaches, including ODE, ABM, and IRL modeling.

Walter Mebane

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My primary project, election forensics, concerns using statistical analysis to try to determine whether election results are accurate. Election forensics methods use data about voters and votes that are as highly disaggregated as possible. Typically this means polling station (precinct) data, sometimes ballot box data. Data can comprises hundreds of thousands or millions of observations. Geographic information is used, with geographic structure being relevant. Estimation involves complex statistical models. Frontiers include: distinguishing frauds from effects of strategic behavior; estimating frauds probabilities for individual observations (e.g., polling stations); adjoining nonvoting data such as from in-person election observations.

Brendan Kochunas

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Brendan Kochunas is an Assistant Professor in the Department of Nuclear Engineering and Radiological Science. Dr. Kochunas work focus on high performance computing methods, especially parallel algorithms for the 3D Boltmann Transport Equation. He is the lead developer and primary author of the MPACT (Michigan Parallel Characterstics based Transport) code. Currently, leading the development of MPACT and its application within CASL (www.casl.gov) constitutes his research activities.

Dr. Kochunas is a co-director of the Center for Scientific Software Infrastructure, and the lead instructor of MICDE course Methods and Practice of Scientific Computing. He has created a novel and integrated class curriculum that immerse U-M students in many HPC tools and resources, and teaches them to effectively use these in scientific computing research.

Victoria Booth

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Victoria Booth is a Professor in the Department of Mathematics and an Associate Professor in the Department of Anesthesiology. Her interdisciplinary research in mathematical and computational neurosciences focuses on constructing and analyzing biophysical models of neurons and neural networks in order to quantitatively probe experimental hypothesis and provide experimentally-testable predictions. Her research provides continuous reciprocal interactions between modeling and experimental results.

Prof. Booth and her colleagues are constructing neurophysiologically based models of the neuronal networks and neurotransmitter interactions in the brainstem and the hypothalamus that regulate wake and sleep states. She is also addressing the question of the influence of intrinsic neuron properties and network topology on the generation of spatio-temporal activity patterns in large-scale neural networks.

C. Alberto Figueroa

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Alberto Figueroa is a Professor with a joint appointment in Biomedical Engineering and Vascular Surgery. He works on computational methods for patient-specific cardiovascular simulation.figueroa_image-264x300

Modeling the function of the cardiovascular system in health and disease represents a fascinating scientific challenge. This challenge can only be addressed by combining a deep understanding of the physiologic, biologic, engineering and mathematical principles involved.Our lab uses medical image processing, high performance computational fluid dynamics simulation, and multi-scale modeling to simulate blood flow in the human body. Our specific areas of interest are surgical planning, disease research, arterial growth and remodeling, and medical device design and performance evaluation.

Quentin Stout

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Most of his research and teaching involves parallel computing of some form: design of scalable algorithms and data structures; applications to numerous scientific problems such as a large multidisciplinary team modeling space weather or a small interdisciplinary group doing imputation on datasets of social preferences; and performance analysis, both experimental and analytical.  These projects have used a variety of computer architectures, ranging from tens to hundreds of thousands of cores. He also works on algorithms for abstract fine-grain parallel computer models motivated by concerns such as time/number-of-processors/peak-power tradeoffs and the constraints imposed by the fact that computation is done in 2- or 3-dimensional space. Further, he develops serial algorithms for optimizing adaptive sampling problems such as adaptive clinical trials, algorithms for isotonic regression, and various other computer science problems.

Paul Zimmerman

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From elementary chemical reactions to exciton dynamics in solar cells, chemistry is a particularly rich field for atomistic simulation. Research in the Zimmerman group develops and employs a broad spectrum of computational techniques to chemical problems. Special emphasis is taken on creating new, practical computational methods for application to problems that are considered out-of-reach to standard simulation methodologies. For instance, automated prediction of chemical reactions has long been considered impossible using quantum chemical simulation. To break this limitation, the Zimmerman group is creating new techniques for locating reaction paths and products of catalytic reactions, with the goal of predicting the outcome of reactions prior to experiment. These tools use a combination of chemical intuition, applied mathematics, and massively parallel computation to achieve an impressive level of automation and predictive value.

Automatically generated growth pattern of a chemical reaction network involving a hydrogen storage material, NH3BH3.

Automatically generated growth pattern of a chemical reaction network involving a hydrogen storage material, NH3BH3.

Emmanouil (Manos) Kioupakis

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His group uses first-principles computational methods and high-performance computing resources to predictively model the structural, electronic, and optical properties of bulk materials and nanostructures. The goal is to understand, predict, and optimize the properties of novel electronic, optoelectronic, photovoltaic, and thermoelectric materials.

The Kioupakis group uses high-performance computing to predictively model the electronic and optical properties of semiconductor nanostructures such as nanoporous silicon, nitride nanowires, and novel 2D materials.

The Kioupakis group uses high-performance computing to predictively model the electronic and optical properties of semiconductor nanostructures such as nanoporous silicon, nitride nanowires, and novel 2D materials.

Sherif El-Tawil

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Prof. El-Tawil’s general research interest lies in computational modeling, analysis, and testing of structural materials and systems. He is especially interested in how buildings and bridges behave under the extreme loading conditions generated by manmade and natural hazards such as seismic excitation, collision by heavy objects, and blast. The focus of his research effort is to investigate how to utilize new materials, concepts and technologies to create innovative structural systems that mitigate the potentially catastrophic effects of extreme loading.

Much of his research is directed towards the computational and theoretical aspects of structural engineering, with particular emphasis on computational simulation, constitutive modeling, multiscale techniques, macro-plasticity formulations, nonlinear solution strategies and visualization methods. Prof. El-Tawil also has a strong and long-sustained interest in multi-disciplinary research. He has conducted research in human decision making and social interactions during extreme events and the use of agent based models for egress simulations. He is also interested in visualization and has developed new techniques for applying virtual reality in the field of finite element simulations and the use of augmented reality for rapid assessment of infrastructure damage in the wake of disasters.
Modeling the collapse response of a 10-story building.

Modeling the collapse response of a 10-story building.