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.

Portrait of Monica Valluri

Monica Valluri

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Her research is based on the theoretical framework of Galactic Dynamics. Two profoundly mysterious unseen components of galaxies are central supermassive black holes and dark matter halos (massive, invisible halos of matter whose presence is inferred only from their gravitational effects on visible objects like stars.) Dr. Valluri uses galactic dynamics to interpret and model motions of stars observed with state-of-the-art telescopes using new and powerful numerical methods. Her work has led to important insights into how these dark components influence the structure and evolution of galaxies. Some of the topics she is currently working on include:

  • accurately measuring the masses of supermassive black holes in any type of  galaxy, their effects on their host galaxies, and their role in galaxy evolution;
  • understanding the orbital structure of stellar bars in spiral galaxies and their interactions with supermassive black holes
  • the properties (such as space and velocity distribution) of the mysterious “dark matter” that constitutes most of the mass in the Universe;
  • understanding the dynamical structure of the Milky Way Galaxy from the properties of tidal streams, and the orbits of stars in the Milky Way’s halo;
  • the role of non-linear dynamical processes (e.g. chaos and dynamical relaxation) in sculpting galaxies.

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.

Siqian Shen

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Prof. Shen’s research derives multifaceted mathematical optimization models for decision making under data uncertainty and information ambiguity. The models she considers often feature stochastic parameters and discrete (0-1) decision variables. The goal is to seek optimal solutions for balancing risk and cost objectives associated with complex systems. She also develops efficient algorithms for solving the large-scale optimization models, based on integer programming, stochastic and data-driven approaches, and special network topologies. In particular, her research has been applied to cyberinfrastructure design and operations management problems related to power grids, transportation, and Cloud Computing systems.

A sensor monitored network for research allocation and routing in highly uncertain environments (e.g., post-disaster delivery, highly congested traffic system, or high-demand computing network). The network is structured by solving a general mathematical optimization model.

A sensor monitored network for research allocation and routing in highly uncertain environments (e.g., post-disaster delivery, highly congested traffic system, or high-demand computing network). The network is structured by solving a general mathematical optimization model.

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.