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.

Photo of Robert Dick

Robert Dick

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Robert Dick is an Associate Professor in the Department of Electrical Engineering and Computer Science, in the Electrical and Computer Engineering division. He also co-founded and served as CEO of Stryd, Inc., which produces wearable electronics for athletes. He received his Ph.D. degree from Princeton University in 2002 and his B.S. degree from Clarkson University in 1996. He worked as a Visiting Professor at Tsinghua University’s Department of Electronic Engineering in 2002, as a Visiting Researcher at NEC Labs America in 1999, and was on the faculty of Northwestern University from 2003-2008.

Prof. Dick has published in the areas of embedded operating systems, data compression, embedded system synthesis, dynamic power management, low-power and temperature-aware integrated circuit design, wireless sensor networks, human perception aware computer design, reliability, embedded system security, and behavioral synthesis. He especially likes projects in which a deep understanding of a particular application leads to a new fundamental concept or technology with broader application. He is a principal investigator in MICDE’s catalyst grant titled “Embedded Machine Learning Systems To Sense and Understand Pollinator Behavior”.

He received an NSF CAREER award and won his department’s Best Teacher of the Year award in 2004. In 2007, his technology won a Computerworld Horizon Award and his paper was selected as one of the 30 in a special collection of DATE papers appearing during the past 10 years. His 2010 work won a Best Paper Award at DATE.

Laura Balzano

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Laura Balzano is an Associate 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.

Cynthia Finelli

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Cynthia Finelli is a Professor in the department of Electrical Engineering and Computer Science, ECE division, and in the School of Education. Her research area is engineering education. She is the Founding Director of the Center for Research and Learning in Engineering at Michigan, which supports college-wide initiatives in engineering teaching and learning, and currently serves as its Faculty Director for Engineering Education Research. Her current research focus on student’s resistance to active learning, faculty adoption of evidence-based teaching practices, student teams in the engineering classroom, and institutional change. Prof. Finelli is a member of MICDE’s Education committee whose goal is to continuously review and develop the institute’s educational programs and campus wide teaching of computational sciences, in and out of the classrooms.

Eric Michielssen

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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.


Electromagnetic analysis of computer board and metamaterial.

Jeff Fessler

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Jeff Fessler is a Professor in the Department of Electrical Engineering and Computer Science – Electrical and Computer Engineering Division. His research interests include numerical optimization, inverse problems, image reconstruction, computational imaging, tomography, magnetic resonance imaging.  Most of these applications involve large problem sizes and parallel computing methods (cluster, cloud, GPU, SIMD, etc.) are needed.

Cynthia Chestek

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Cynthia Chestek is an Associate Professor of Biomedical Engineering, Electrical Engineering – Electrical and Computer Engineering Division, and the Neurosciences Graduate Program.

The Chestek lab focuses on brain machine interface (BMI) systems using 100 channel arrays implanted in motor and pre-motor cortex. The goal of this research is to eventually develop clinically viable systems to enable paralyzed individuals to control prosthetic limbs, as well as their own limbs using functional electrical stimulation and assistive exoskeletons. The lab apply a variety of machine learning algorithms to large-scale neural datasets obtained from spiking activity or field potentials in order to decode the motor commands. This is done both offline, and in real-time during experiments. Other computational challenges include mitigating non-stationarities in neural recordings over time. Over the next few decades, the size of these datasets is most likely to increase with the development of larger electrode arrays, and novel surgical techniques for implanting them.

Heath Hofmann

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Heath Hofmann is a Professor of Electrical Engineering and Computer Science – Electrical and Computer Engineering Division. Professor Hofmann’s computational research focuses on the modelling of electromechanical devices and systems. An area of emphasis is the development of computationally efficient electromagnetic and thermal models of rotating electric machines based upon finite element analysis (FEA). Specific projects include the development of parallelizable preconditioners for steady-state magnetoquasistatic FEA solvers, the application of model-order-reduction techniques to thermal and electromagnetic finite-element models, nonlinear modeling of magnetic materials, integrated FEA-circuit simulations, and the development of “scaling” techniques that allow the user to efficiently create a suite of electric machines with different performance characteristics from a single design.

Magnetoquasistatic model of permanent magnet machine

Magnetoquasistatic model of permanent magnet machine