Dr. Avestruz is a computational cosmologist. She uses simulations to model, predict, and interpret observed large-scale cosmic structures. Her primary focus is to understand the evolution of galaxy clusters. These are the most massive gravitationally collapsed structures in our universe, comprised of hundreds to thousands of galaxies. Other aspects of her work prepare for the next decade of observations, which will produce unprecedented volumes of data. In particular, she is leading software development efforts within the clusters working group of the Large Synoptic Survey Telescope to calibrate galaxy cluster masses from simulation data. Dr. Avestruz also incorporates big data methods, including machine learning, to extract gravitational lensing signatures that probe the mass distribution of massive galaxies and galaxy clusters.

Rohini Bala Chandran is an Assistant Professor in the Department of Mechanical Engineering. Her research focuses on developing computational models integrated with experimental analyses to probe the interplay of heat and mass transfer, fluid flow, and chemical reactions that play a central role in a host of thermal, thermochemical, and electrochemical energy systems. Her group leverages high-performance computing to understand coupled transport and kinetic phenomena, and to perform multi-parameter optimization to design and operate efficient devices for energy conversion and storage. Current research activities in her group focus on solar fuels from water and carbon dioxide, thermal energy storage at high temperatures, and wastewater treatment.

Phani Motamarri is an Assistant Research Scientist in the department of Mechanical Engineering. His research interests lie in the broad scope of computational materials science with emphasis on computational nano-science leading to applications in the areas of mechanics of materials and energy. His research is strongly multidisciplinary, drawing ideas from applied mathematics, data science, quantum-mechanics, solid-mechanics, materials science and scientific computing.

The current research focus lies in developing systematically improvable real-space computational methodologies and associated mathematical techniques for conducting large-scale electronic-structure (ab-initio) calculations -via- density functional theory (DFT). Massively parallel and scalable numerical algorithms using finite-elements (DFT-FE) are developed as a part of this research effort, which enabled large-scale DFT calculations on tens of thousands of atoms for the first time using finite-element basis. These computational methods will aid fundamental studies on defects in materials, molecular and nanoscale systems which otherwise would have been difficult to study with the existing state of the art computational methods. Current areas of application include — (a) first-principles modelling of energetics of point defects and dislocations in Al, Mg and its alloys which are popular in light-weighting applications to provide useful inputs to meso-scale and continuum models, (b) providing all-electron DFT input to advanced electronic structure approaches like the GW method for accurate prediction of electronic properties in semiconductor-materials.

Brendan Kochunas is an Assistant Research Scientist 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 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.

Jesse Capecelatro is an Assistant Professor in the Department of Mechanical Engineering. His research is focused on developing large-scale simulation capabilities for prediction and design of the complex multi-physics and multiphase flows relevant to energy and the environment. To achieve this, his group develops robust and scalable numerical methods to leverage world-class supercomputing resources. Current research activities include adjoint-based sensitivity of turbulent combustion, modeling strongly-coupled particle-laden flows, and multiphase aeroacoustics.

Don Siegel is an Associate Professor affiliated with the Mechanical Engineering Department and the Department of Material Science and Engineering. His research targets the discovery, characterization, and understanding of novel materials for energy-related applications. These efforts primarily employ atomic scale modeling to predict thermodynamic properties and kinetics. These data provide the necessary ingredients for identifying performance limiting mechanisms and for the “virtual screening” of candidate compounds having desired properties. Prof. Siegel is currently exploring several varieties of energy storage materials, lightweight structural alloys, and materials suitable for use in carbon capture applications.

Eric Michielssen is the Louise Ganiard Johnson Professor of Electrical Engineering and Computer Science – Electrical and Computer Engineering Division.

His research interests include all aspects of theoretical, applied, and computational electromagnetics, with emphasis on the development of fast (primarily) integral-equation-based techniques for analyzing electromagnetic phenomena. His group studies fast multipole methods for analyzing static and high frequency electronic and optical devices, fast direct solvers for scattering analysis, and butterfly algorithms for compressing matrices that arise in the integral equation solution of large-scale electromagnetic problems.

Furthermore, the group works on plane-wave-time-domain algorithms that extend fast multipole concepts to the time domain, and develop time-domain versions of pre-corrected FFT/adaptive integral methods. Collectively, these algorithms allow the integral equation analysis of time-harmonic and transient electromagnetic phenomena in large-scale linear and nonlinear surface scatterers, antennas, and circuits.

Recently, the group developed powerful Calderon multiplicative preconditioners for accelerating time domain integral equation solvers applied to the analysis of multiscale phenomena, and used the above analysis techniques to develop new closed-loop and multi-objective optimization tools for synthesizing electromagnetic devices, as well as to assist in uncertainty quantification studies relating to electromagnetic compatibility and bioelectromagnetic problems.

His research makes use of rich information contained in the spectrally resolve observations (chiefly from space) to probe the climate system and gauge the performance of climate models. Topics of his ongoing projects include formulation and design of climate monitoring system based on accurate in-flight calibration system, spectrally resolved radiation budget and radiative feedbacks, detecting spectral signals of climate changes, and model evaluations using spectral data set. In the course of such studies, huge amount of data sets from observations or climate model simulations are fed into radiative transfer model to general spectral radiances at thousands of channels for each grid on the globe and for each time interval. To accurately and efficiently carry out such calculation is only possible with massive high performance computing and, as of today, such task is still computationally challenging.

Dr. Toth works on algorithm and code development for space and plasma physics simulations. He has a leading role in the development of the Space Weather Modeling Framework (SWMF) that can couple and execute about a dozen different space physics models modeling domains from the surface of the Sun to the upper atmosphere of the Earth. He is one of the main developers of the BATS-R-US code, a multi-physics and multi-application magnetohydrodynamics code using block-adaptive grids. He is collaborating with many colleagues and students using the SWMF and BATSRUS for a wide range of applications: solar corona, coronal mass ejections, magnetic storms, comets, moons (Titan, Enceladus), planetary magnetospheres (Earth, Venus, Mars, Jupiter, Saturn), interaction of moons with their plasma environment (Titan, Enceladus), interaction of comets with the solar wind, outer heliosphere interaction with the inter-stellar material, etc. The SWMF is used by the Community Coordinated Modeling Center (CCMC) at NASA Goddard for research as well as real-time forecasting of space weather. Dr. Toth was also the software architect for the Center for Radiative Shock Hydrodynamics (CRASH). This DoE funded center worked on modeling radiative shocks created by high energy lasers and the uncertainty quantification of the model results. He has designed and implemented of the Versatile Advection Code, a general purpose publicly available hydrodynamics and MHD code. VAC has been used by hundreds of researchers around the world to simulate various hydrodynamic and MHD problems.

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.