Yasser Aboelkassem

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Prof. Aboelkassem’s laboratory uses stochastic multiscale computational models based on Brownian-Langevin dynamics principles to derive a coarse-graining model that can describe the cardiac thin-filament activation process during contraction. The model links atomistic molecular simulations of protein-protein interactions in the thin-filament regulatory unit to sarcomere-level activation dynamics. We first calculate the molecular interaction energy between tropomyosin and actin surface using Brownian dynamics simulations. This energy profile is then generalized to account for the observed tropomyosin transitions between its regulatory stable states. The generalized energy landscape then served as a basis for developing a filament-scale model using Langevin dynamics.

A Stochastic Multiscale Model of Cardiac Thin Filament Activation Using Brownian-Langevin Dynamics

Zhenguang Huang

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Dr. Zhenguang Huang is an assistant research scientist in the department of Climate and Space Sciences and Engineering. His research focus is on 3-D global five- and six-moment multi-fluid simulations, and 3-D global multi-ion MHD simulations of the space plasma. He is also one of the main developers of the Block-Adaptive-Tree-Solarwind-Roe-Upwind-Scheme (BATSRUS) and the Space Weather Modeling Framework (SWMF).

Wenhao Sun

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Dr. Sun’s research group is interested in resolving outstanding fundamental scientific problems that impede the computational materials design process. The group uses high-throughput density functional theory, applied thermodynamics, and materials informatics to deepen our fundamental understanding of synthesis-structure-property relationships, while exploring new chemical spaces for functional technological materials. These research interests are driven by the practical goal of the U.S. Materials Genome Initiative to accelerate materials discovery, but whose resolution requires basic fundamental research in synthesis science, inorganic chemistry, and materials thermodynamics.

Lulu Zhao

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Dr. Lulu Zhao is an assistant research scientist in the Climate and Space Sciences and Engineering. Her research focus is the modelling of the acceleration and transport of energetic particles using M-FLAMPA (multiple-field-line-advection-model with particle acceleration). The M-FLAMPA model extract magnetic field lines from the magnetohydrodynamics (MHD) simulations of the background solar wind solution through SWMF (space weather frame work). Along those magnetic field lines, the particle transport and acceleration are modelled by solving the particle transport equations.

 

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.

Photo of Mackillo Kira

Mackillo Kira

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Professor Kira develops systematic many-body and quantum-optics approaches to quantitatively analyze, guide, and explain contemporary experiments that study phenomena encountered in the broad field of quantum sciences.  His typical research effort involves extensive collaborations with experimentalists to rigorously test quantum concepts and designs. As few demonstrations, his team has recently discovered dropletons, a quasiparticle accelerator, quantum-memory effects, quantum interferences in high-harmonic generation, and explained quantum depletion in strongly interacting Bose-Einstein condensates.

Professor Kira’s research interests are: Quantum optoelectronics, semiconductor quantum optics, quantum optics, condensed-matter theory, terahertz spectroscopy, many-body interactions, photon correlations, coherent and ultrafast phenomena, and cluster-expansion approach.

Seymour M.J. Spence

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Dr. Spence’s main research thrusts are focused on the theoretical and computational development of models and frameworks for the implementation and adoption in practice of performance-based wind engineering, optimization of structural systems subject to uncertainty and experimental/stochastic wind loads, and metamodeling of nonlinear and dynamic structural systems under uncertainty. Specific areas in which Dr. Spence’s research group have made contributions are: performance-based wind engineering, system-level analysis and optimization of uncertain dynamic systems, probabilistic modeling and uncertainty propagation, metamodeling of static and dynamic systems, machine learning in stochastic analysis of structures, resilience and adaptation of communities subject to severe wind events, topology optimization of uncertain stochastic systems, and computational fluid dynamics for wind and rain simulation.

Computational fluid dynamics simulation of wind driven rain in hurricanes

Joseph N.S. Eisenberg

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Dr. Eisenberg is the John G. Searle endowed Chair and Professor of Epidemiology in the School of Public Health at the University of Michigan.  Dr.  Eisenberg received his PhD in Bioengineering in the joint University of California, Berkeley/University of California, San Francisco program, and an MPH from the School of Public Health at the University of California, Berkeley.  Dr. Eisenberg studies infectious disease epidemiology with a focus on waterborne and vectorborne diseases. His broad research interests, global and domestic, integrate theoretical work in developing disease transmission models and empirical work in designing and conducting epidemiology studies. He is especially interested in the environmental determinants of infectious diseases.

One of Dr. Eisenberg’s research focus has been on the development of a new microbial risk assessment framework that shifts the traditional approach of individual-based static models to population-based dynamic models. In coordination with the Environmental Protection Agency (EPA), this work has led him to apply these disease transmission models to assess the public health risk from exposures to microbial agents in drinking waters, recreational waters, and biosolids. Dr. Eisenberg’s work locally and abroad is highly collaborative and interdisciplinary.

Portrait of Jeremy Bricker

Jeremy Bricker

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Jeremy Bricker is an Associate Professor in the department of Civil and Environmental Engineering. His research is focused on hydraulic engineering to investigate the resilience of structures and infrastructure exposed to both increasing hazard due to climate change and increasing consequences due to expansion of development in coastal and flood-prone areas.

Computational methods are useful in hydraulic engineering for assessing the safety of coastal and hydraulic structures, estimating the flood risk experienced by communities, and predicting damage to buildings during floods, hurricanes, and tsunamis. At a large scale of hundreds to thousands of kilometers, shallow water equation models simulate tsunami propagation, storm surge and wave generation, and river flood occurrence. At scales of kilometers to tens of kilometers, these models resolve overland inundation due to flood events, allowing empirical or analytical estimates of forces on structures and damage to buildings and infrastructure. At a small scale of tens to hundreds of meters, computational fluid dynamics (CFD) directly calculates pressures and forces on submerged and emergent structures from floodwaters and waves. This can be linked with a dynamic response model to assess whether resonance could lead to structural failure, or linked with a Finite Element Method (FEM) model to assess stresses within the structure. Such modeling is useful for forensic analysis of the failure of bridges, buildings, and other infrastructure after floods, as well as for planning and design of new structures.

 

Streamlines around the cross-section of a 3-girder bridge deck submerged by a river flood, from Oudenbroek et al. (2018).