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Rohini Bala Chandran

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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.

Temperature distributions in a solar thermochemical reactor for water and carbon dioxide splitting predicted by computational modeling.

Stephen Smith

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Stephen Smith is an Assistant Professor in the Department of Ecology and Evolutionary Biology. The Smith lab group is primarily interested in examining evolutionary processes using new data sources and analysis techniques. They develop methods to address questions about the rates and modes of evolution using the large data sources (e.g., genomes and transcriptomes) that have become more common in the biological disciplines over the last ten years. In particular, they use DNA sequence data to construct phylogenetic trees and conduct analyses about processes that shape the evolution of lineages and their genomes using these trees. In addition to this research program, they also address how new data sources can facilitate new research in evolutionary biology. To this end, they sequence transcriptomes, primarily in plants, with the goal of better understanding where, within the genome and within the phylogeny, processes like gene duplication and loss, horizontal gene transfer, and increased rates of molecular evolution occur.

Alex Gorodetsky

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Alex Gorodetsky is an Assistant Professor in the Department of Aerospace Engineering. His research includes using applied mathematics and computational science to enhance autonomous decision making under uncertainty. His research has an emphasis on controlling systems that must act in complex environments that are often represented through expensive computational simulations. His research uses tools from wide ranging areas including uncertainty quantification, statistical inference, machine learning, numerical analysis, function approximation, control, and optimization. Several of the key areas he focuses on are: optimal planning by solving large scale Markov decision processes, fast Bayesian estimation for nonlinear dynamical systems, high-dimensional compression and approximation of physical quantities of interest, and fusion of information from varying simulation fidelities and data through multi-fidelity modeling.

Wei Lu

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Wei Lu is Professor in the Department of Mechanical Engineering. His research interests lie in the modeling and simulation of the evolution of nano and microstructures. His research group studies the mechanics involved in nano and micro systems, as well as the mechanical properties and performance of advanced materials. These include the joining of dissimilar materials, the fretting wear of structures, and electrochemical mechanical processes in battery systems. They use multi-scale analysis, and machine learning techniques combined with experimental methods to design and optimize new materials.
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Trachette Jackson

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Trachette L. Jackson is Full Professor in the Mathematics Department, who specializes in Computational Cancer Research or Mathematical Oncology.   A focus of Dr. Jackson’s research has been achieving a unified understanding of how signaling molecules, cells, and micro-environmental structures coordinate to control blood vessel generation, morphology and functionality during tumor growth.  Her work aims to biochemically and biomechanically characterize the collective motion vascular endothelial cells, one of most important cell types involved in cancer development due to their role in angiogenesis.

With an eye toward addressing critical challenges associated with targeted molecular therapeutics, for example determining which drugs are the best candidates for clinical trials, Dr. Jackson also develops multiscale mathematical models that are designed to optimize the use of targeted drug treatment strategies.  These mathematical models connect the molecular events associated with tumor growth and angiogenesis with the temporal changes in tumor cell and endothelial cell proliferation, migration and survival, and link these dynamics to tumor growth, vascular composition, and therapeutic outcome.

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Bryan Goldsmith

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Bryan Goldsmith is an Assistant Professor in the Department of Chemical Engineering. His works focus on the development of novel catalysts and materials. The world is facing a growing population, mass consumerism, and rising greenhouse gas levels, all the while people strive to increase their standard of living. Computational modeling of catalysts and materials, and making use of its synergy with experiments, facilitates the process to design new systems since it provides a valuable way to test hypotheses and understand design criteria. His research team focuses on obtaining a deep understanding of catalytic systems and advanced materials for use in sustainable chemical production, pollution abatement, and energy generation. They use first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., statistical learning and data mining) to extract key insights of catalysts and materials under realistic conditions, and to help create a platform for their design.

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2)

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2)

Scott Lempka

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Scott Lempka is an Assistant Professor in the Department of Biomedical Engineering and director of the Neuromodulation Laboratory. The Neuromodulation Lab focuses on clinical neurostimulation (a.k.a. neuromodulation) therapies, such as spinal cord stimulation and deep brain stimulation. These therapies are used to treat a variety of neurological disorders, such as chronic pain and Parkinson’s disease. In these therapies, metal electrodes are used to apply electrical pulses that override pathological activity in the nervous system. The Neuromodulation Lab develops computer models of the electric fields generated by the stimulation and the direct neural response. These computer models are combined with clinical data, such as quantitative sensory testing and functional neuroimaging, to understand the effects of various therapies – why they work in some patients and not in others.

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Matthew Kay

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Matthew Kay is an Assistant Professor in the School of Information. His research focus in on human–computer interaction and information visualization. He tackles problems using a multi-faceted approach, including qualitative and quantitative analysis of behavior, building and evaluating interactive systems, and designing and testing visualization techniques.

Laura Balzano

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Laura Balzano is an Assistant Professor in Electrical Engineering and Computer Science at the University of Michigan. She is an Intel Early Career Faculty Honor Fellow and received an NSF BRIGE award. She received all her degrees in Electrical Engineering: BS from Rice University, MS from the University of California in Los Angeles, and PhD from the University of Wisconsin. She received the Outstanding MS Degree of the year award from the UCLA EE Department, and the Best Dissertation award from the University of Wisconsin ECE Department. Her main research focus is on modeling with highly incomplete or corrupted data, and its applications in networks, environmental monitoring, and computer vision. Her expertise is in statistical signal processing, matrix factorization, and optimization.

Jon Zelner

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Jon Zelner is an Assistant Professor in the Dept. of Epidemiology and Center for Social Epidemiology and Population Health in the UM School of Public Health. His work focuses on understanding the joint contributions of social, biological, and environmental factors to infectious disease transmission dynamics, with a particular focus on Tuberculosis (TB) transmission in high-burden contexts.

To do this, Jon uses mathematical and individual-based models to guide the design of studies and statistical tools for extracting information on infectious disease transmission from real-world spatiotemporal data. This ranges from small-scale simulation of household and community-based transmission to large-scale individual-based models of infectious disease transmission in megacities. A recurring methodological theme of this work is the challenge in navigating the tradeoff between fidelity to real-world processes and the need for parsimonious explanation of observable phenomena.

Tuberculosis hotspot in Lima, Peru

A hotspot of elevated incidence of multi-drug resistant tuberculosis (MDR-TB) in Lima, Peru is shown in red. Points indicate the location of TB cases; those marked ‘x’ are MDR-TB cases.