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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.

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”.

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.

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.

Brian Umberger

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Brian Umberger is a professor of Movement Science in the School of Kinesiology. Professor Umberger’s research is focused on the biomechanics, energetics, and control of locomotion in humans and other bipeds. A major emphasis of his group is developing computational models of muscle and the musculoskeletal system, and using these models to study bipedal locomotion. Applications have ranged from fundamental studies of locomotion energetics, to the restoration of mobility in gait disorders, and the evolutionary basis for human bipedalism. His research often involves solving large-scale optimal control problems, which present a number of computational challenges. Past work has focused on topics such as parallel global optimization and efficient numerical evaluation of large, sparse Jacobian matrices. Current interests include bi-level and multi-objective optimization approaches, and stochastic methods for evaluating simulation results. The research is often cross-disciplinary in nature, involving teams of scientists, engineers and clinicians.

Musculoskeletal model of a person with lower limb amputation for optimizing prosthesis design

Stephen Smith

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Stephen Smith is an Associate 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.

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.


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.



Heather Mayes

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Heather Mayes is an Assistant Professor in the Department of Chemical Engineering. Her research group uses multi-scale modeling to discover protein-sugar interactions and to harness them for renewable energy and improved health. The study of carbohydrate-protein interactions is an important step to create renewable fuels and chemicals from non-food biomass, and the results can be applied to several human diseases, including cancer and autoimmune disorders. Prof. Mayes uses computational tools in her research, including quantum mechanics, molecular dynamics, and rare-event sampling methods. She collaborates with experimental groups to understand past and guide future wet-lab studies to advance renewable chemicals and fuels, as well as disease understanding.


Multiscale simulation to uncover mechanisms behind protein-sugar interactions, such as how the T. reesi Cel6A enzyme coordinates making and breaking four bonds for cellulose hydrolysis.



Angela Violi

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Angela Violi is a Professor in the Department of Mechanical Engineering, and adjunct faculty in Chemical Engineering, Biophysics, Macromolecular Science and Engineering, and Applied Physics. The research in the group of Violi is focused on the application of statistical mechanics and computational methods to chemically and physically oriented problems in nanomaterials and biology. The group investigates the formation mechanisms of nanomaterials for various applications, including energy and biomedical systems, and the dynamics of biological systems and their interactions with nanomaterials.