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).



Fred Feng

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Dr. Fed Feng is an Assistant Professor of Industrial and Manufacturing Systems Engineering at the University of Michigan-Dearborn. Dr. Feng’s research methods include behavioral data analysis, statistical learning, human performance modeling, human factors, and human-machine interaction. Dr. Feng’s research applications are mainly in the transportation safety domain, more specifically, driver behavior and modeling, cycling safety, pedestrian safety, human-centered mobility, and sustainable transportation.

Carlos Aguilar

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The schematic is a series of muscle images during the regenerative process, whereby resident stem cells repair the tissue.

Skeletal muscle contains multiple cell types, regenerates when damaged via a pool of resident stem cells (called satellite cells), consumes significant amounts of metabolic energy, grows and adapts its structure and function based on its environment. Nearly all actions in life are defined by the coordinated actions of skeletal muscle but when afflicted by injury or aging, muscle function decreases and quality of life is reduced. Currently, there are little to no therapies for recovery after severe trauma or age-associated muscle wasting (sarcopenia). Prof. Aguilar’s laboratory focuses on studying molecular mechanisms satellite cells use after trauma and aging with a particular focus on transcriptional and epigenetic regulation. We develop and utilize different types of high-throughput sequencing based assays and sophisticated bioinformatics algorithms to generate these insights.

Albert S. Berahas

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Albert S. Berahas is an Assistant Professor of Industrial & Operations Engineering. Dr. Berahas received a BSC in Operations Research and Industrial Engineering from Cornell University, and his MSC and Ph.D.degrees in Engineering Sciences and Applied Mathematics from Northwestern University. Before coming to the University of Michigan, Dr. Berahas held positions as a Postdoctoral Research Fellow at Lehigh University and at Northwestern University.

Dr. Berahas’ research broadly focuses on designing, developing and analyzing algorithms for solving large scale nonlinear optimization problems. Such problems are ubiquitous, and arise in a plethora of areas such as engineering design, economics, transportation, robotics, machine learning and statistics. Specifically, he is interested in and has explored several sub-fields of nonlinear optimization such as: (i) general nonlinear optimization algorithms, (ii) optimization algorithms for machine learning, (iii) constrained optimization, (iv) stochastic optimization, (v) derivative-free optimization, and (vi) distributed optimization.

Salar Fattahi

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Today’s real-world problems are complex and large, often with an overwhelmingly large number of unknown variables which render them doomed to the so-called “curse of dimensionality”. For instance, in machine learning, it is important to obtain simple, interpretable, and parsimonious models of high-dimensional and noisy data. As another example, in large-scale dynamic systems, a long standing question is how to efficiently design distributed control policies for unknown and interconnected systems.

Our research is centered around two main objectives: (1) to model these problems as tractable optimization problems; and (2) to develop structure-aware and scalable computational methods for solving these optimization problems that come equipped with certifiable optimality guarantees. At the core of our research is to show that exploiting hidden structures in these problems—such as graph-induced or spectral sparsity—is a key game-changer in the pursuit of massively scalable and guaranteed computational methods.


Our research interests lies at the intersection of optimization, data analytics, and control.

Hugo Casquero

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Hugo Casquero is an Assistant Professor in the Mechanical Engineering Department at University of Michigan – Dearborn. His research is focused on developing accurate, robust, and efficient computational methods and using them to solve a myriad of open problems in fluid mechanics, solid mechanics, fluid-structure interaction, biomechanics, and multiphysics. The overarching theme of the computational methods that Dr. Casquero develops is to solve partial differential equations exploiting the new advantages that splines bring to computational mechanics. Dr. Casquero is particularly interested in developing computational frameworks for real-world applications in which experimental measurement of the quantities of interest is too costly or not currently available. Current research activities in his group include achieving a seamless integration between design and analysis of thin-walled structures, studying the dynamics of vesicles, capsules, red blood cells, and droplets under different types of flow, and developing structure-preserving spline discretizations of magnetohydrodynamics to solve problems in fusion energy.

animation of a crash simulation plotting von Mises stress

Crash simulation plotting von Mises stress. A discretization of Kirchhoff-Love shells based on analysis-suitable T-splines is used. This simulation includes elastoplastic material behavior, fracture criteria, contact algorithms, and spot-weld modeling. Material failure takes place around the largest hole of the B-pillar.  

Joshua Stein

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Josua Stein is an associate professor of Ophthalmology and Visual Sciences at Michigan Medicine. He is the principal investigator of the Sight Outcomes Research Collaborative (SOURCE) consortium, a group of academic ophthalmology departments who are extracting EHR data and data from ocular diagnostic tests for all eye care recipients at their respective institutions, removing all PHI, and sending their data to the University of Michigan where him and his team are cleaning and aggregating the data and making it available to researchers at the various sites for research and Q/I projects. The team is integrating data from SOURCE into machine learning algorithms, applying systems engineering techniques to forecast disease trajectory for chronic eye diseases such as glaucoma, and other precision medicine initiatives.