Marisa Eisenberg

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Marisa Eisenberg is an Associate Professor in the Department of Epidemiology, and in the Department of Mathematics. Her research revolves around mathematical epidemiology, focus on using and developing parameter estimation and identifiability techniques to model disease dynamics. Her group builds multi-scale models of infectious disease, including HPV, cholera and other environmentally driven diseases.

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Likelihood surface exhibiting issues of unidentifiability—colors indicate goodness-of-fit, and the white line shows the values taken by an optimization algorithm as it navigates the surface.

Rafael Meza

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Rafael Meza is an Assistant Professor in the Department of Epidemiology, School of Public Health, and an Honorary Professor at the Mexico National Institute of Public Health (INSP). Dr. Meza’s is interested in cancer risk assessment and the analysis of cancer epidemiology data using mechanistic models of carcinogenesis. He is also interested in the mathematical modeling of chronic and infectious disease dynamics and its applications in public health policy design.

Dr. Meza is Coordinating Principal Investigator of the Cancer Intervention and Surveillance Modeling Network (CISNET) lung group, Co-Leader of the Cancer Epidemiology and Prevention Program of the Cancer Prevention and Control Program at the University of Michigan Comprehensive Cancer Center (UMCCC), and member of the UM Tobacco Research Network.

Currently, Dr. Meza is developing models to evaluate the impact of screening and smoking cessation on lung cancer risk. Additional projects include the development of methodologies to investigate the effects of infectious disease dynamics on the risk of cancers with infectious disease etiology, modeling the impact of policies on cigarette and smokeless tobacco use, and modeling the impact of diabetes prevention strategies in Mexico.

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Smoking prevalence

Denise Kirschner

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Denise Kirschner is a Professor in the Department of Microbiology and Immunology.  She serves as founding co-director of the Center for Systems Biology, is affiliated with both the Center for the Study of Complex Systems and  the Center for Computational Medicine and Bioinformatics. Her research involves the modeling of immunological responses in infectious diseases, focusing on questions related to host-pathogen interactions. The pathogens she studies include both bacteria (Mycobacterium tuberculosis) and HIV-1. Such pathogens have evolved strategies to evade or circumvent the host-immune response and the lab’s goal is to understand the complex dynamics involved and develop optimal treatment and vaccine strategies.

Michael Wellman

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In a strategic environment, agents face decisions where the outcomes depend on the behavior of other autonomous agents. The strategic reasoning group develops techniques for understanding and engineering complex multiagent environments, using concepts and methods from economics as well as computer science. Specifically, we apply game-theoretic principles to data from large-scale agent-based simulation, in an approach called empirical game-theoretic analysis (EGTA). EGTA combines simulation, machine learning, and other empirical methods to reason about the strategic issues in complex multiagent settings. We are particularly interested in domains characterized by dynamism, networks, and uncertainty, including applications in financial markets, information security, and sustainable transportation.

The iterative empirical game-theoretic analysis process.

The iterative empirical game-theoretic analysis process.

Aaron King

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Aaron A. King is an Associate Professor of Ecology & Evolutionary Biology, and is affiliated with the Department of Mathematics, the Center for the Study of Complex Systems, the Center for Computational Medicine & Bioinformatics, the Fogarty International Center, and the National Institutes of Health. Prof. King develops and applies computationally intensive methods for using stochastic dynamical systems models to learn about infectious disease ecology and epidemiology.  These systems are typically highly noisy and nonlinear and are frequently uncomfortably high-dimensional.  Nevertheless, the King group’s approaches allow them to find out what the data have to say about the mechanisms that generate them.

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Robert Ziff

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Professor Ziff carries out computational and theoretical studies of various physical problems, most notably percolation but also catalysis modeling and several reaction/diffusion systems.  For percolation, he has developed various algorithms that have allowed substantial increases in performance, for the study of threshold behavior, crossing probability, etc. He also studies algorithms for efficiently simulating rare-event simulations such as chemical reactions and diffusion-limited aggregation.

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Inside a diffusion-limited aggregation (DLA) cluster, grown using an accelerated rare-event algorithm.

Brian Arbic

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Brian Arbic is a physical oceanographer. His group focuses on global modeling of internal tides and gravity waves, with growing interests in air-sea interactions and modeling of surface tides and their role in Earth System processes over geological time scales.  Other interests include the dynamics and energy budgets of oceanic mesoscale eddies (the oceanic equivalent of atmospheric weather systems), tsunamis, and paleotsunamis. His group uses in-situ and remotely sensed observations, idealized models, and realistic models.  He collaborates widely with scientists in the US and abroad, and his projects include collaborations with scientists at large modeling centers, such as the US Naval Research Laboratory (NRL), NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL), DOE’s Los Alamos National Laboratory (LANL), Europe’s Mercator Modeling Center, and NASA’s Jet Propulsion Laboratory (JPL).  He participates in NASA missions, including the Surface Water Ocean Topography (SWOT) mission, the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) mission, and the Ocean Surface Topography mission.  Arbic has been a member of the U-M ASC STEM Africa committee since 2012.  He is the principal founder of the Coastal Ocean Environment Summer School in Ghana (https://coessing.org), is the lead on the concept note for “An Ocean Corps for Ocean Science” (https://globaloceancorps.org), and a co-lead on the concept note “EquiSea:  The Ocean Science Fund for All” (https://equisea.org).

The surface expression of the M_2 principal lunar semidiurnal internal tide — the tide that arises due to the stratification of the ocean. The top panel shows analysis of satellite altimetry data, while the bottom shows results from HYCOM, run by collaborators at the Naval Research Laboratory. (Shriver, et al 2012)

The surface expression of the M_2 principal lunar semidiurnal internal tide — the tide that arises due to the stratification of the ocean. The top panel shows analysis of satellite altimetry data, while the bottom shows results from HYCOM, run by collaborators at the Naval Research Laboratory. (Shriver, et al 2012)

Sharon Glotzer

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Sharon Glotzer is a Professor of Chemical Engineering and of Material Science and Engineering. The Glotzer group uses computer simulations to discover the fundamental principles by which nanoscale systems of building blocks self-assemble into higher order, complex, and often hierarchical structures. Their goal is to learn how to manipulate matter at the molecular, nanoparticle, and colloidal scales to create “designer” structures through assembly engineering. Using molecular dynamics and Monte Carlo simulation codes developed in-house for graphics processors (GPUs) and scalable to large hybrid CPU/GPU clusters, they are the leading computational assembly group in the world, with the most powerful codes for studying assembly and packing. Among others, they are the lead developer of HOOMD-Blue, the fastest molecular dynamics code written solely for GPUs and distributed freely as open source software on codeblue.umich.edu.  Based on the fundament scientific principles of assembly gleaned from their studies, they carry out high throughout simulations for materials by design, contributing to the national Materials Genome Initiative.

Shapes can arrange themselves into crystal structures through entropy alone, new computational research from the University of Michigan shows.

Shapes can arrange themselves into crystal structures through entropy alone, new computational research from the University of Michigan shows.