Camille Avestruz

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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.

[Click on image to see video] Image projection of various components and properties of a simulated galaxy cluster in its last 8 gigayears of formation. The top left panel shows the underlying dark matter content, the top middle image shows the distribution of stars, and the remaining four panels are properties of the gas content: density, temperature, entropy, and metallicity. To model the evolution of galaxy clusters in a cosmological volume, the simulation uses adaptive refinement in space and time in order to span the relevant dynamic range of the system.

Aaron Towne

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Aaron Towne is an Assistant Professor in the Department of Mechanical Engineering. His research develops simple models that can be used to understand, predict, and control turbulent fluid dynamical systems. His approach focuses on identifying and modeling coherent flow structures, i.e., organized motions within otherwise chaotic flows. These structures provide building blocks for an improved theoretical understanding of turbulence and also contribute significantly to engineering quantities of interest such as drag, heat transfer, and noise emission. Consequently, strategically manipulating coherent structures can potentially lead to vast performance improvements in a wide range of engineering applications. Realizing this potential requires new data mining and analysis methods that can be used to identify and extract these organized motions from the large data sets produced by high fidelity simulations and experiments, as well as new theoretical and computational approaches for modeling and controlling them. Aaron’s research focuses on developing these tools for turbulent flow applications, while also contributing more broadly to the emerging areas of large-scale data mining and machine learning.

Temperature (color) and pressure (gray scale) from a simulation of a turbulent jet. The pressure field exhibits organized patterns (alternating black and white regions) that can be leveraged to understand, predict, and control the noise produced by the jet. Image courtesy of Guillaume Brès.

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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.

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Fernanda Valdovinos

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Fernanda Valdovinos is an Assistant Professor in the department of Ecology and Evolutionary Biology and Complex Systems. She received her Ph.D. in Ecology and Evolutionary Biology from the Faculty of Science, University of Chile in 2008. Before joining the University of Michigan, she was a researcher in the Estación Biológica de Doñana, Spain, at the Pacific Ecoinformatics and Computational Lab in Berkeley, CA and at the department of Ecology and Evolutionary Biology at the University of Arizona.

Her lab studies the structure and dynamics of ecological networks at ecological and evolutionary scales; including their resilience to biodiversity loss, biological invasions, climate change, and exploitation by humans. She is a principal investigator in MICDE Catalyst Grant: “Embedded Machine Learning Systems To Sense and Understand Pollinator Behavior”.

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Jianzhi (George) Zhang

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Jianzhi (George) Zhang is a Professor of Ecology and Evolutionary Biology interested in the relative roles of chance and necessity in evolution. He got his B. S. from Fudan University in Shanghai, China, and his Ph. D. in Genetics from Pennsylvania State University. He was a  Fogarty postdoctoral fellow at the National Institute of Allergy and Infectious Diseases before moving to the University of Michigan.

Professor Zhang’s research focuses on two main research areas:  (1) yeast as an experimental system for studying evolution, where his research group uses the budding yeast Saccharomyces cerevisiae and its relatives as model organisms to understand a variety of evolutionary processes such as the genetic basis of phenotypic variations among strains and species, or molecular and genomic bases of heterosis; and (2) computational evolutionary genomics where they use evolutionary, genomic, and/or systemic approaches to analyze publicly available data to characterize and understand pleiotropy, robustness, epistasis, gene-environment interaction, gene expression noise, translational regulation, RNA editing, convergent evolution, adaptation, origin of new genes, among-protein evolutionary rate variation, and other important genetic and evolutionary phenomena. Projects may also involve modeling and simulation, including the MICDE catalyst grant project where the team is using deep neural networks to infer molecular phylogenies and extract phylogenetically useful patterns from amino acid or nucleotide sequences, which will help understand evolutionary mechanisms and build evolutionary models for a variety of analyses.

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Yuanfang Guan

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Yuanfang Guan is an associate professor of Computational Medicine and Bioinformatics. She got her B.S. from the University of Hong Kong, and her Ph.D. in Molecular Biology from Princeton University.

Prof. Guan is interested in machine learning in biology and medicine. Her team has written the majority of the best-performing algorithms in DREAM challenges, the largest systems biology benchmark study. Prof. Guan was awarded the ‘Consistent Best Technical Performer’ for her groups achievements in the DREAM challenges and in recognition of the open source software that they have contributed to the bioinformatics field. She is one of the very few people globally who own multiple gold medals in the annual Data Science Bowl by Kaggle.

Her team has written many award-winning deep learning methods. In traditional machine learning, she is the inventor of GuanRank, adaptive GPR and several other algorithms that are often used as the reference algorithms in benchmark studies/challenges.

Xun Huan

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Xun (Ryan) Huan is an Assistant Professor in the Department of Mechanical Engineering. His research broadly revolves around uncertainty quantification, data-driven modeling, and numerical optimization. His expertise focuses on bridging models and data: optimal experimental design, Bayesian methods for statistical inference, uncertainty propagation in high-dimensional settings, and methods that are robust to model misspecification. He seeks to develop efficient computational methods that integrate realistic models with big data, and combine uncertainty quantification with machine learning to enable robust prediction, design, and decision-making. He is interested in collaborative opportunities in various applications that can benefit from a better understanding of uncertainty and modeling. Current research activities include assessing uncertainty in deep neural networks, and developing sequential experimental design methods for improving autonomy.

Optimal experimental design seeks to identify experiments that produce the most valuable data, and can lead to substantial resource savings. For example, in the design of a shock-tube combustion experiment, design condition A maximizes the expected information gain. When Bayesian inference is performed on data from this experiment, we obtain “tighter” posteriors (with less uncertainty) compared to those obtained from suboptimal design conditions B and C.

Raed Al Kontar

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Rael Al Kontar is an Assistant Professor in the department of Industrial & Operations Engineering. His research broadly focuses on developing data analytics and decision-making methodologies specifically tailored for Internet of Things (IoT) enabled smart and connected products/systems. He envisions that most (if not all) engineering systems will eventually become connected systems in the future. Therefore, his key focus is on developing next-generation data analytics, machine learning, individualized informatics and graphical and network modeling tools to truly realize the competitive advantages that are promised by smart and connected products/systems.

Towards smart and connected systems

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.