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DTSTART;TZID=America/Detroit:20210203T100000
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DTSTAMP:20260605T023628
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LAST-MODIFIED:20230905T171258Z
UID:10000443-1612346400-1612353600@micde.umich.edu
SUMMARY:Introduction to SPSS: Data Management
DESCRIPTION:Each section will go over one chapter from the materials at https://cscar.github.io/workshop-spss/ \nSection 1: Basics of SPSS (1/20\, 10am – 12pm) \nSection 2: Variables (1/27\, 10am – 12pm) \nSection 3: Data Management (2/3\, 10am – 12pm) \nSection 4: Basic Statistical Analysis (2/10\, 10am – 12pm) \nIt is strongly encouraged to have SPSS installed on your machine. If you are accessing SPSS through AppsAnywhere on Virtual Sites\, then you will need to set up a link to your Google Drive\, Box\, or Dropbox storage.
URL:https://micde.umich.edu/event/introduction-to-spss-data-management/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210127T140000
DTEND;TZID=America/Detroit:20210127T170000
DTSTAMP:20260605T023628
CREATED:20230905T171256Z
LAST-MODIFIED:20230905T171256Z
UID:10000015-1611756000-1611766800@micde.umich.edu
SUMMARY:Using GPUs with Python
DESCRIPTION:Python is the Lingua Franca of Data today and is being increasingly used in scientific computations. This workshop introduces Python GPU tools for porting and writing code that runs on GPUs. The primary tools\, Numba and CuPy\, are presented with examples. This workshop is presented by Kristopher Keipert of NVIDIA.\nThis event is open to students\, faculty\, and staff within the University of Michigan community. A Jupyter notebook is used along with a set of lecture slides. The workshop will use online tools\, so there is no need to install any software ahead of time.\nThis event is brought to you by the Michigan Institute for Computational Discovery and Engineering\, and Consulting for Statistics\, Computing & Analytics Research at the University of Michigan in partnership with NVIDIA.\nSpace is limited\, register today!
URL:https://micde.umich.edu/event/using-gpus-with-python-2/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210127T140000
DTEND;TZID=America/Detroit:20210127T170000
DTSTAMP:20260605T023629
CREATED:20230905T171256Z
LAST-MODIFIED:20230905T171256Z
UID:10000423-1611756000-1611766800@micde.umich.edu
SUMMARY:Using GPUs with Python
DESCRIPTION:To view registration information for the February 24\, 2021 session of this workshop\, visit the event page. \n \nPython is the Lingua Franca of Data today and is being increasingly used in scientific computations. This workshop introduces Python GPU tools for porting and writing code that runs on GPUs. The primary tools\, Numba and CuPy\, are presented with examples. Back by popular demand\, this workshop is presented by Kristopher Keipert of NVIDIA. \nThis event is open to students\, faculty\, and staff within the University of Michigan community. A Jupyter notebook is used along with a set of lecture slides. The workshop will use online tools\, so there is no need to install any software ahead of time. \nThis event is brought to you by the Michigan Institute for Computational Discovery and Engineering\, and Consulting for Statistics\, Computing & Analytics Research at the University of Michigan in partnership with NVIDIA. \nSpace is limited\, register today!
URL:https://micde.umich.edu/event/using-gpus-with-python-3/
CATEGORIES:Featured Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210127T100000
DTEND;TZID=America/Detroit:20210127T120000
DTSTAMP:20260605T023629
CREATED:20230905T171256Z
LAST-MODIFIED:20230905T171256Z
UID:10000442-1611741600-1611748800@micde.umich.edu
SUMMARY:Introduction to SPSS: Variables
DESCRIPTION:Each section will go over one chapter from the materials at https://cscar.github.io/workshop-spss/ \nSection 1: Basics of SPSS (1/20\, 10am – 12pm) \nSection 2: Variables (1/27\, 10am – 12pm) \nSection 3: Data Management (2/3\, 10am – 12pm) \nSection 4: Basic Statistical Analysis (2/10\, 10am – 12pm) \nIt is strongly encouraged to have SPSS installed on your machine. If you are accessing SPSS through AppsAnywhere on Virtual Sites\, then you will need to set up a link to your Google Drive\, Box\, or Dropbox storage.
URL:https://micde.umich.edu/event/introduction-to-spss-variables/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210126T150000
DTEND;TZID=America/Detroit:20210126T160000
DTSTAMP:20260605T023629
CREATED:20230905T171256Z
LAST-MODIFIED:20230905T171256Z
UID:10000406-1611673200-1611676800@micde.umich.edu
SUMMARY:MICDE Seminar: Tianle Yuan\, Associate Research Scientist\, University of Maryland\, Baltimore County\, JCET\, NASA Goddard Space Flight Center
DESCRIPTION:About Dr. Tianle Yuan: Dr. Yuan received his B.S. in Geophysics and Computer Science from Peking University\, his Ph.D. from the University of Maryland\, College Park in 2008. After graduation\, he became affiliated with the Joint Center for Earth Systems Technologies (JCET) at the University of Maryland Baltimore County (UMBC) and NASA Goddard Space Flight Center (GSFC) as an Associate Research Scientist. His research interests include cloud and aerosol climate feedback\, aerosol-cloud interactions\, remote sensing\, cloud physics\, and application of ML/Deep Learning in Earth science. In deep learning applications\, Dr. Yuan published a few papers in modeling sub-grid clouds\, global scale clouds\, hurricane prediction\, finding ship-tracks\, and supervised and unsupervised cloud morphology classifications. \nARTIFICIAL INTELLIGENCE-BASED CLOUD DISTRIBUTOR (AI-CD): MODELING CLOUDS AT DIFFERENT SCALES\nHere we introduce the artificial intelligence-based cloud distributor (AI-CD) approach to generate cloud fields across different scales and cloud types. We show that generative adversarial nets (GANs) can not only generate realistic cloud fields with corresponding meteorological variables\, but also capture known physical relationship between cloud fields and meteorological variables such as sea surface temperature\, atmospheric stability\, and relative humidity etc. We demonstrate that this approach works across a large range of spatial scales: from individual grid points (sub-grid process modeling)\, multiple grids\, to global scale. In addition\, the AI-CD approach is stochastic in nature. We suggest the AI-CD approach can be used as a data-drive framework for stochastic cloud parameterization. \n\nThe MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nRegister to immediately receive Zoom details. Note: you may register after the event has started. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-tianle-yuan-research-associate-nasa-goddard-space-flight-center/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Tianle-Yuan.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210126T100000
DTEND;TZID=America/Detroit:20210126T120000
DTSTAMP:20260605T023629
CREATED:20230905T171256Z
LAST-MODIFIED:20230905T171256Z
UID:10000429-1611655200-1611662400@micde.umich.edu
SUMMARY:Introduction to Python's NumPy library
DESCRIPTION:This workshop will introduce you to the NumPy library in Python\, which is useful in scientific computing. We will cover NumPy’s n-dimensional array object and associated functions in depth\, along with related linear algebra and random number capabilities. Some familiarity with Python is expected. The workshop will be done online via Zoom. We will run the code using Google Colab\, which requires a Google account.
URL:https://micde.umich.edu/event/introduction-to-pythons-numpy-library-4/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210122T150000
DTEND;TZID=America/Detroit:20210122T163000
DTSTAMP:20260605T023629
CREATED:20230905T171256Z
LAST-MODIFIED:20230905T171256Z
UID:10000446-1611327600-1611333000@micde.umich.edu
SUMMARY:GIS Fundamentals – II (Vector and network data models)
DESCRIPTION:This is the second workshop in a series of workshops we are offering this semester on the fundamentals of GIS. Each workshop covers one or two key elements of GIS and is self-contained. The focus is on conceptual details that can provide sufficient preparation for applications\, but we will also touch upon the technical aspects. Most workshops will have at least one hands-on exercise. The first one hour of the workshop is a lecture-style presentation\, followed by the next half-hour for the hands-on exercises. Unless mentioned otherwise\, we will use R. \nHow data is recorded in a digital system has significant implications for accuracy\, algorithms\, and the type of analyses that can be undertaken.  In this workshop we will cover data structure for vector and network data in the context of a 2-D GIS system. The focus is on developing a basic understanding of elements such as essential primitives\, how more complex objects are derived from the primitives\, and different formats and file systems.
URL:https://micde.umich.edu/event/gis-fundamentals-ii-vector-and-network-data-models/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210120T100000
DTEND;TZID=America/Detroit:20210120T120000
DTSTAMP:20260605T023629
CREATED:20230905T171256Z
LAST-MODIFIED:20230905T171256Z
UID:10000441-1611136800-1611144000@micde.umich.edu
SUMMARY:Introduction to SPSS: Basics of SPSS
DESCRIPTION:Each section will go over one chapter from the materials at https://cscar.github.io/workshop-spss/ \nSection 1: Basics of SPSS (1/20\, 10am – 12pm) \nSection 2: Variables (1/27\, 10am – 12pm) \nSection 3: Data Management (2/3\, 10am – 12pm) \nSection 4: Basic Statistical Analysis (2/10\, 10am – 12pm) \nIt is strongly encouraged to have SPSS installed on your machine. If you are accessing SPSS through AppsAnywhere on Virtual Sites\, then you will need to set up a link to your Google Drive\, Box\, or Dropbox storage.
URL:https://micde.umich.edu/event/introduction-to-spss-basics-of-spss/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210119T160000
DTEND;TZID=America/Detroit:20210119T170000
DTSTAMP:20260605T023629
CREATED:20230905T171257Z
LAST-MODIFIED:20230905T171257Z
UID:10000427-1611072000-1611075600@micde.umich.edu
SUMMARY:MICDE Seminar: Yang Liu\, Research Scientist\, Scalable Solvers Group of the Computational Research Division at the Lawrence Berkeley National Laboratory
DESCRIPTION:About Dr. Liu: Yang Liu is a research scientist in the Scalable Solvers Group of the Computational Research Division at the Lawrence Berkeley National Laboratory\, in Berkeley\, California. Dr. Liu received the Ph.D. degree in electrical engineering from the University of Michigan in 2015. From 2015 to 2017\, he worked as a postdoctoral fellow at the Radiation Laboratory\, University of Michigan. From 2017 to 2019\, he worked as a postdoctoral fellow at the Lawrence Berkeley National Laboratory. His main research interest is in numerical linear and multi-linear algebras (including sparse solvers\, randomized low-rank\, butterfly and tensor algebras)\, computational electromagnetics (including fast iterative time-domain integral equation solvers\, fast direct integral and differential equation solvers\, and multi-physics\nmodeling)\, scalable machine learning algorithms\, and high-performance scientific computing. Dr. Liu authored and co-authored the Sergei A. Schelkunoff Transactions Prize Paper\, APS 2018\, second place student paper\, ACES 2012\, and the first place student paper\, FEM 2014. \nFAST\, DIRECT INTEGRAL DIFFERENTIAL EQUATION SOLVERS FOR ELECTROMAGNETIC ACOUSTIC\, AND ELASTIC APPLICATIONS AT ALL FREQUENCY RANGES: Large-scale and full-wave modeling for acoustic and elastic inversion applications\, analysis and synthesis of electromagnetic systems for traditional and emerging RF\, microwave\, terahertz applications rely on efficient numerical tools. Integral equation (i.e.\, method of moment) and differential equation (e.g.\, finite-difference\, finite-element\, and finite-volume) formulations lead to dense and sparse linear systems\, respectively. These linear systems can be solved by either iterative or direct solvers. Iterative solvers\, despite their success in constructing well-conditioned formulations and fast multipole-type algorithms\, remain inefficient for systems that are inherently ill-conditioned and/or require multiple right-hand sides. This is particularly true for design automation\, inverse scattering\, and other coupled systems where iterative solvers often require forbiddingly high iteration time. Direct solvers\, in stark contrast\, can attain reliable solutions in a predictable time. However\, exact direct solvers typically require O(N 3 ) and O(N 2 ) computational costs for dense and sparse systems of size N\, respectively. Fast direct solvers\, on the other hand\, rely on the fact that off-diagonal blocks of the well-ordered linear systems can be compressed by numerical linear algebra tools including low-rank and butterfly decompositions. When further embedded in hierarchical matrix frameworks\, such as H-matrix\, hierarchically off-diagonal low-rank (HODLR)\, and hierarchically semi-separable (HSS) formats\, these direct solvers and preconditioners can achieve quasi-linear complexities for construction\, factorization and solution for the discretized systems across all frequency ranges. We will review the development of these solvers in the past two decades\, with an emphasis on their butterfly-based variants and distributed-memory parallelization for high-frequency problems. An open source package integrating most techniques reviewed\, called ButterflyPACK\, will also be introduced. \n\nWatch the full webinar. \nNote: You can register after the webinar has started.
URL:https://micde.umich.edu/event/micde-aim-seminar-yang-liu-research-scientist-scalable-solvers-group-of-the-computational-research-division-at-the-lawrence-berkeley-national-laboratory/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/11/Yang-Liu.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210118T010000
DTEND;TZID=America/Detroit:20210118T160000
DTSTAMP:20260605T023629
CREATED:20230905T171257Z
LAST-MODIFIED:20230905T171257Z
UID:10000437-1610931600-1610985600@micde.umich.edu
SUMMARY:Advanced Graphics Optimization For Data Visualization In Unity3D
DESCRIPTION:Modern 3D game engines and computer hardware can render convincing graphics\, rivaling that of pre-rendered 3D animation. But video games still require special optimization techniques and tricks. This relates directly to perceived capabilities for data visualization and serious applications: we can generate and render thousands of interactive objects. But what about millions? \nThis workshop will go over different techniques to render as many objects as possible at once in Unity3D\, with the context of visualizing data as a point-cloud. Examples will include (but not be limited to) GPU Instancing\, Unity’s Particle System\, and Compute Shaders. It is strongly recommended that attendees be familiar with Unity3D prior to this workshop to get the most out of the session.
URL:https://micde.umich.edu/event/advanced-graphics-optimization-for-data-visualization-in-unity3d-2/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210112T150000
DTEND;TZID=America/Detroit:20210112T163000
DTSTAMP:20260605T023629
CREATED:20230905T171257Z
LAST-MODIFIED:20230905T171257Z
UID:10000445-1610463600-1610469000@micde.umich.edu
SUMMARY:GIS Fundamentals – I (Coordinate systems)
DESCRIPTION:This is the first workshop in a series of workshops we are offering this semester on the fundamentals of GIS. Each workshop covers one or two key elements of GIS and is self-contained. The focus is on conceptual details that can provide sufficient understanding for applications\, but we will also touch upon the technical aspects. Most workshops will have at least one hands-on exercise. Typically\, each workshop is divided into one hour of lecture-style presentation and half an hour of hands-on exercises. Unless mentioned otherwise\, we will use R for the hands-on portion. \n\nThere are 100s of coordinate systems and datums available in modern software that provide GIS functionalities. A basic understanding of different coordinate systems\, their strength and limitations\, and conversion between different systems are essential for choosing the right system and manipulating geographically referenced data. In this workshop we will cover the basics of coordinate systems for 2-D GIS from an applied perspective.
URL:https://micde.umich.edu/event/gis-fundamentals-i-coordinate-systems/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210111T090000
DTEND;TZID=America/Detroit:20210111T160000
DTSTAMP:20260605T023629
CREATED:20230905T171257Z
LAST-MODIFIED:20260401T194925Z
UID:10000433-1610355600-1610380800@micde.umich.edu
SUMMARY:Software Carpentry Workshop @WISE: Shell\, R\, Git
DESCRIPTION:Please note: This is a two day workshop. January 11 and January 12  (*one* registration covering both days) \nSoftware Carpentry aims to help researchers get their work done in less time and with less pain by teaching them basic research computing skills in a supportive & inclusive environment. This hands-on workshop will cover basic concepts and tools\, including R for plotting\, the Unix shell\, version control with Git & GitHub\, R for data analysis\, and writing reports with R Markdown. \nThis special Software Carpentry workshop is sponsored by the U-M Women in Science and Engineering and is taught entirely by women. \nThe workshop is aimed at graduate students and other researchers\, but anyone can participate. You don’t need to have any previous knowledge of the tools that will be presented at the workshop. \nVirtual (Zoom link to be provided to registrants)
URL:https://micde.umich.edu/event/software-carpentry-workshop-wise-shell-r-git/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201208T170000
DTEND;TZID=America/Detroit:20201208T190000
DTSTAMP:20260605T023629
CREATED:20230905T171257Z
LAST-MODIFIED:20230905T171257Z
UID:10000426-1607446800-1607454000@micde.umich.edu
SUMMARY:Spatial regression models
DESCRIPTION:This lecture-style workshop will introduce relevant concepts and techniques for modelling cross-sectional data observed on regular (such as remote sensing pixels) or irregular (such as Census polygons) lattice. Such data often exhibits spatial dependence and is common across several fields. \n\nWe will cover the following topics: motivating examples from time series; spatial random fields and stationarity; spatial autocorrelation measures including Moran’s I; neighborhood or adjacency matrices that capture spatial dependence; and spatial autoregressive models including their estimation and interpretation. \n\nPlease note that the material will be discussed in a lecture style presentation with little or no hands-on components.  You should know linear regression well.
URL:https://micde.umich.edu/event/spatial-regression-models/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201208T150000
DTEND;TZID=America/Detroit:20201208T170000
DTSTAMP:20260605T023629
CREATED:20230905T171257Z
LAST-MODIFIED:20230905T171257Z
UID:10000425-1607439600-1607446800@micde.umich.edu
SUMMARY:Using Distill for R Markdown
DESCRIPTION:There are a variety of formats available for R markdown beyond ‘standard html’\, one of which is Distill.  Distill is specifically oriented toward presentation of results\, and offers a clean look with additional capabilities for citations\, marginal exposition\, and more.  One can even build an entire website or blog with this format.  This workshop will provide a brief overview and demonstration.\n\nhttps:m-clark.github.io
URL:https://micde.umich.edu/event/using-distill-for-r-markdown/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201208T100000
DTEND;TZID=America/Detroit:20201208T120000
DTSTAMP:20260605T023629
CREATED:20230905T171254Z
LAST-MODIFIED:20230905T171254Z
UID:10000386-1607421600-1607428800@micde.umich.edu
SUMMARY:Introduction to Deep Neural Networks with Keras/TensorFlow
DESCRIPTION:Deep Neural Networks (DNNs) are used as a machine learning method for both regression and classification problems. Keras is a high-level\, Python interface running on top of multiple neural network libraries\, including the popular library TensorFlow. In this workshop\, participants will learn how to quickly use the Keras interface to perform nonlinear regression and classification with standard fully-connected DNNs\, as well as image classification using Convolutional Neural Networks (CNNs). We will also look at regularization techniques and how to deal with under- and over-fitting. All examples will use Python; some familiarity with Python is recommended. The workshop will be done online via BlueJeans. We will run the models using Google Colab\, which requires a Google account.
URL:https://micde.umich.edu/event/introduction-to-deep-neural-networks-with-keras-tensorflow-9/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201203T150000
DTEND;TZID=America/Detroit:20201203T160000
DTSTAMP:20260605T023629
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000401-1607007600-1607011200@micde.umich.edu
SUMMARY:MICDE / IOE Seminar: Salar Fattahi\, Assistant Professor\, Industrial & Operations Engineering\, University of Michigan
DESCRIPTION:About Salar Fattahi: Dr. Salar Fattahi is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan. He received his M.S. and Ph.D. degrees in Industrial Engineering and Operations Research from UC Berkeley. He received a M.S. degree from Columbia University\, and a B.S. degree from Sharif University of Technology\, Iran\, both in Electrical Engineering. Salar’s research lies at the intersection of optimization\, data analytics\, and control theory. He was the recipient of several awards\, including the 2020 INFORMS ENRE Best Student Paper Award\, 2018 INFORMS Data Mining Best Paper Award and 2020 Power & Energy Society General Meeting Best-of-the-Best Paper Award. He was also a finalist for the 2018 American Control Conference Best Paper Award. \nWebinar: LARGE-SCALE INFERENCE OF TIME-VARYING MARKOV RANDOM FIELDS: BRIDGING THE GAP BETWEEN STATISTICAL AND COMPUTATIONAL EFFICIENCIES \nContemporary systems are comprised of a massive number of interconnected components that interact according to a hierarchy of complex\, dynamic\, and unknown topologies. For example\, with billions of neurons and hundreds of thousands of voxels\, the human brain is considered as one of the most complex physiological networks\, whose structure remains as a long-standing mystery. As another example\, the emergence of self-driving cars has only accentuated the need for the development of real-time and reliable methods for detecting moving objects\, whose temporal locations are captured through a dynamically-evolving 3D network. Nonetheless\, the vast amounts of parameters to be estimated\, caused both by the large number of components and the time-varying nature of the systems\, are currently the major bottlenecks in our ability to successfully solve such inference problems. \nThe temporal behavior of today’s interconnected systems can be captured via time-varying Markov random fields (MRF). A popular approach to achieve this goal is based on the so-called maximum-likelihood estimation (MLE): to find a probabilistic graphical model\, based on which the observed data is most probable to occur. The MLE-based methods suffer from several fundamental drawbacks which render them impractical in realistic settings. First\, they often suffer from notoriously high computational cost in the massive problems\, where the number of variables to be inferred is in the order of millions\, or more. Second\, they fail to efficiently incorporate prior structural information into their estimation procedure. With the goal of bridging this knowledge gap\, the aim of this work is to revisit the standard MLE as the “Holy Grail” of the inference methods for graphical models\, and precisely pinpoint and remedy the scenarios where it fails. A recurring theme in our proposed approach is a class of efficiently-solvable mixed-integer optimization problems that is used in lieu of the regularized MLE for the inference of time-varying MRFs. Our proposed optimization problems enjoy strong statistical and computational guarantees\, while being amenable to a wide class of graphical models with different side information\, such as sparsity\, smoothness\, etc. \n\nThe MICDE Fall 2020 and Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis event will be a joint seminar with the University of Michigan College of Engineering’s Industrial Operations & Engineering department. \nQuestions? Email MICDE-events@umich.edu \nConnect via this Zoom link: https://umich.zoom.us/j/96516676892#success
URL:https://micde.umich.edu/event/micde-ioe-seminar-salar-fattahi-assistant-professor-industrial-operations-engineering-university-of-michigan/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Salar-Fattahi.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201201T100000
DTEND;TZID=America/Detroit:20201201T120000
DTSTAMP:20260605T023629
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000424-1606816800-1606824000@micde.umich.edu
SUMMARY:Introduction to Machine Learning
DESCRIPTION:OVERVIEW\n\n\nMachine learning is becoming an increasingly popular tool in several fields\, including data science\, medicine\, engineering\, and business. This workshop will cover basic concepts related to machine learning\, including definitions of basic terms\, sample applications\, and methods for deciding whether your project is a good fit for machine learning. No prior knowledge or coding experience is required \nINSTRUCTORS\nMeghan Richey\nMachine Learning Specialist\nInformation and Technology Services – Advanced Research Computing – Technology Services \nMeghan Richey is a machine learning specialist in the Advanced Research Computing- Technology Services department at the University of Michigan. She consults on several faculty and student machine learning applications and research studies\, specializing in natural language processing and convolutional neural networks. Before her position at the university\, Ms. Richey worked for a defense contractor as a software engineer to design and implement software solutions for DoD-funded artificial intelligence efforts. \nMATERIALS\n\n\n\nIntroduction to Machine Learning Topics\n\n\nA Zoom link will be provided to the participants the day before the class. Registration is required. \n\n\nInstructor will be available at the Zoom link\, to be provided\, from 9-10 AM for computer setup assistance. \nPlease note\, this session will be recorded.   \n\nRegister here \nIf you have questions about this workshop\, please send an email to the instructor at richeym@umich.edu
URL:https://micde.umich.edu/event/introductions-to-machine-learning-2/
LOCATION:Your Desktop
CATEGORIES:Data Science,Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201120T150000
DTEND;TZID=America/Detroit:20201120T160000
DTSTAMP:20260605T023629
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000404-1605884400-1605888000@micde.umich.edu
SUMMARY:MICDE / AIM Seminar: Baole Wen\, Assistant Professor\, Mathematics\, University of Michigan
DESCRIPTION:About Baole Wen: Dr. Wen obtained a B.S. degree in Engineering Mechanics and a M.S. degree in Fluid Mechanics\, respectively\, from the Beijing University of Aeronautics and Astronautics.  He was awarded a CEPS Graduate Fellowship \& a Dissertation Year Fellowship and earned a Ph.D. in Applied Mathematics from University of New Hampshire in 2015.  His Ph.D. research was focused on understanding the underlying flow and transport mechanisms governing the spatiotemporally-chaotic system of porous medium convection at large Rayleigh numbers.  Upon graduation\, he was awarded a Peter O’Donnell\, Jr. Postdoctoral Fellowship through the Oden Institute for Computational Engineering and Sciences in the University of Texas at Austin.  His primary research interests are fluid dynamics\, mathematical modeling\, scientific computing and dynamical systems theory.  Recently\, he is working with Dr. Charles Doering as a Postdoctoral Assistant Professor at University of Michigan on extreme behavior in fundamental models of fluid mechanics. \nSTEADY COHERENT STATES IN RAYLEIGH–B\'{E}NARD CONVECTION: Buoyancy-driven flows are central to engineering heat transport\, atmosphere and ocean dynamics\, climate science\, geodynamics\, and stellar physics.   Rayleigh–B\’enard convection—the buoyancy driven flow in a fluid layer heated from below and cooled from above—is recognized as the simplest scenario in which to study such phenomena\, and beyond its importance for applications this problem has served for a century as one of the primary paradigms of nonlinear physics\, complex dynamics\, pattern formation and turbulence.   A central question about Rayleigh–B\’enard convection is how the Nusselt number $Nu$ depends on the Rayleigh number $Ra$ and the Prandtl number $Pr$—i.e.\, how heat flux depends on imposed temperature gradient and the ratio of the fluid’s kinematic viscosity to its thermal diffusivity—as $Ra\rightarrow\infty$.  Experiments and simulations have yet to rule out either `classical’ $Nu \sim Ra^{1/3}$ or `ultimate’ $Nu \sim Ra^{1/2}$ asymptotic scaling.  Here we provide clear quantitative evidence suggesting that the ultimate regime might not exist.  Our tactic is to study relatively simple time-independent states called rolls and compare heat transport by these rolls with that of turbulent convection.  These steady rolls are not typically seen in large-$Ra$ simulations or experiments because they are dynamically unstable.  Nonetheless\, they are part of the global attractor for the infinite-dimensional dynamical system defined by Rayleigh’s model\, and recent results suggest that steady rolls may be one of the key coherent states comprising the `backbone’ of turbulent convection.  By developing novel numerical methods\, we compute steady rolls between no-slip boundaries for $Ra\le 10^{14}$ with $Pr=1$ and various horizontal periods.  We find that rolls of the periods that maximize $Nu$ at each $Ra$ have classical $Nu\sim Ra^{1/3}$ scaling asymptotically\, and they transport more heat than turbulent experiments or simulations at similar parameters.  If turbulent heat transport continues to be dominated by steady transport asymptotically\, it cannot achieve ultimate scaling. \n\nThe MICDE Fall 2020 and Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis event will be a joint seminar with the University of Michigan Applied Interdisciplinary Mathematics. \nQuestions? Email MICDE-events@umich.edu \nJoin the webinar via the Zoom details below:\nhttps://umich.zoom.us/j/96450383843 \nMeeting ID: 964 5038 3843\nPasscode: 010182
URL:https://micde.umich.edu/event/micde-aim-seminar-baole-wen-assistant-professor-mathematics-university-of-michigan/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Baole-Wen.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201119T133000
DTEND;TZID=America/Detroit:20201119T160000
DTSTAMP:20260605T023629
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000396-1605792600-1605801600@micde.umich.edu
SUMMARY:Map making in R
DESCRIPTION:The focus of the workshop is twofold: to learn cartography principles for generating single and multivariable choropleth maps\, and explore functionalities of R for generating static and interactive web maps. We will use the COVID19 data available at (https://github.com/nytimes/covid-19-data) and combine it with information from Census and other sources to visualize spatial patterns and make maps. Participants should be proficient in R and vector data GIS.
URL:https://micde.umich.edu/event/map-making-in-r/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201116T130000
DTEND;TZID=America/Detroit:20201116T160000
DTSTAMP:20260605T023629
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000389-1605531600-1605542400@micde.umich.edu
SUMMARY:Advanced Graphics Optimization For Data Visualization In Unity3D
DESCRIPTION:BlueJeans link will be shared with registered attendees 24 hours before start \nModern 3D game engines and computer hardware can render convincing graphics\, rivaling that of pre-rendered 3D animation. But video games still require special optimization techniques and tricks. This relates directly to perceived capabilities for data visualization and serious applications: we can generate and render thousands of interactive objects. But what about millions? \nThis workshop will go over different techniques to render as many objects as possible at once in Unity3D\, with the context of visualizing data as a point-cloud. Examples will include (but not be limited to) GPU Instancing\, Unity’s Particle System\, and Compute Shaders. It is strongly recommended that attendees be familiar with Unity3D prior to this workshop to get the most out of the session.
URL:https://micde.umich.edu/event/advanced-graphics-optimization-for-data-visualization-in-unity3d/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201112T110000
DTEND;TZID=America/Detroit:20201112T120000
DTSTAMP:20260605T023629
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000403-1605178800-1605182400@micde.umich.edu
SUMMARY:MICDE Seminar: Denise Kirschner\, Professor\, Microbiology and Immunology\, University of Michigan Medical School
DESCRIPTION:About Denise Kirschner: Dr. Kirschner received her Bachelors\, Masters and PhD in applied mathematics from Tulane University. She did graduate work also at Los Alamos National Labs and a postdoctoral fellowship at Vanderbilt University joint with the departments of Mathematics and Infectious Diseases. Over the past 25 years Dr. Kirschner has focused on questions related to models of host-pathogen interactions in infectious diseases. Her main focus has been to build models of persistent infections (e.g. Helicobacter pylori and Mycobacterium tuberculosis and HIV-1). Her goal is to understand the complex dynamics involved\, together with how perturbations to this interaction (via treatment with chemotherapies or immunotherapies) can lead to prolonged or permanent health. For the past 20 years\, her research focus has been on building multi-scale models to describe the host immune response to M. tuberculosis at multiple spatial and time scales and in multiple physiological compartments including lung\, lymph nodes and blood. \nTo date\, she have worked and collaborated with experimentalists generating data on TB with mouse\, non-human primate and human studies. Denise has over 150 publications in top journals describing this work that spans topics from methodological to biological advancement. Dr. Kirschner currently serves (and has for the past 17 years) as Editor-in-Chief of the Journal of Theoretical Biology. She serves as the founding co-director of The Center for Systems Biology at the University of Michigan\, an interdisciplinary center at the University of Michigan aimed to facilitate research and training between wet-lab and theoretical scientists. In 2016 she was elected as President-elect of the Society for Mathematical Biology and has served as its president from 2017-2020. Denise’s passion for mentoring students\, postdoctoral fellows and junior faculty has been a major focus of her career\, and her key mission is to promote both mathematics and family values in the scientific community.\n \nAPPROACHES FOR STUDYING MULTISCALE COMPUTATIONAL MODELS:  \nMycobacterium tuberculosis is a bacterium that infects 1/3 of the world today. While only 10% of infected individuals experience active tuberculosis disease\, if left untreated infection results in death. The remainder of individuals harbor the bacteria in a clinically latent infection\, and those individuals can experience reactivation of infection up to 10% per year. Our goal in a number of studies is to understand the role of the bacteria in initiating\, sustaining and inhibiting the immune response during infection. Granulomas are a hallmark of tuberculosis infection arising within lungs of infected humans. Understanding the immune response that leads to formation of granulomas can help us better design therapies to control or clear infection. We use a hybrid multi-scale approach that is fine grained for spatial details to help uncover these dynamics paired with a coarse grained spatial model that allows us to capture the entire host dynamics. We use a combination of statistic and mathematical and engineering approaches to predict optimal treatments. \n\nThe MICDE Fall 2020 and Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nWatch the full webinar here. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-denise-kirschner-professor-microbiology-and-immunology-university-of-michigan-medical-school/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Denise-Kirschner.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201110T100000
DTEND;TZID=America/Detroit:20201110T120000
DTSTAMP:20260605T023629
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000385-1605002400-1605009600@micde.umich.edu
SUMMARY:Image Segmentation using Deep Learning with FastAI
DESCRIPTION:This workshop will demonstrate how to perform image segmentation using the FastAI [fast.ai] Python library\, which is built on the deep learning library PyTorch. Some familiarity with Python is expected\, but no previous experience with FastAI or PyTorch is needed. The workshop will be done online via BlueJeans. We will run the code using Google Colab\, which requires a Google account.
URL:https://micde.umich.edu/event/image-segmentation-using-deep-learning-with-fastai/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201026T140000
DTEND;TZID=America/Detroit:20201026T153000
DTSTAMP:20260605T023629
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000415-1603720800-1603726200@micde.umich.edu
SUMMARY:Stata: Data Manipulation
DESCRIPTION:This is a series of workshops designed to introduce participants to the Stata software. No prior experience with Stata is required. The sections are:\n\nSection 1: The Basics of Stata – Interacting with Stata. (10/19 2-3:30)\nSection 2: Working with Data Sets – Importing\, opening\, and saving data sets. (10/21 2-3:30)\nSection 3: Data Management – The basics of maintaining and exploring a data set. (10/23 2-3:30)\nSection 4: Data Manipulation – Creating and modifying variables and other ways of manipulating your data. (10/26 2-3:30)\n\nYou do not need to attend all sessions; however\, the sessions build on each other and it will be assumed you are familiar with the material in earlier sessions. The workshop materials can be found at https://cscar.github.io/workshop-stata-intro/ for review.\n\nIt is strongly encouraged that you have a current version of Stata (version 16) available on your local computer\, though not required. You may access Stata through midesktop (https://midesktop.umich.edu/) if needed. If you have an older version of Stata\, some material may not work on your system.
URL:https://micde.umich.edu/event/stata-data-manipulation/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201026T130000
DTEND;TZID=America/Detroit:20201026T160000
DTSTAMP:20260605T023629
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000388-1603717200-1603728000@micde.umich.edu
SUMMARY:Data Visualization With 3D Graphics Using Unity3D and C#
DESCRIPTION:BlueJeans link will be shared with registered attendees 24 hours before start \nVideo game development is more accessible than ever before thanks to modern software tools\, with many options free to download. These tools are also used to program more “serious” applications that require interactive 3D graphics\, from mobile apps\, virtual and augmented reality\, computer vision and artificial intelligence\, and real-time CGI film production. \nUnity3D is a powerful and popular game engine for both hobbyist and professional projects\, able to compile a ‘game’ to almost any computer platform\, and free to download for non-commercial use. This workshop will show how you can use it to render data from research projects in a 3D interactive representation for user analysis and demonstration. \nIn this workshop\, we introduce the Unity3D workspace\, and prepare a demo that allows the user to load an example dataset and view it as a simple set of 3D representations. A basic familiarity with any computer programming language (C# will be used during the session) is recommended to get the most out of the workshop. To take part\, users will be responsible to use their own laptop with Unity3D (available for Windows\, Macintosh and Linux) pre-installed. Additional project files will be provided to registered users ahead of the workshop date.
URL:https://micde.umich.edu/event/data-visualization-with-3d-graphics-using-unity3d-and-c-3/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201023T140000
DTEND;TZID=America/Detroit:20201023T153000
DTSTAMP:20260605T023629
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000414-1603461600-1603467000@micde.umich.edu
SUMMARY:Stata: Data Management
DESCRIPTION:This is a series of workshops designed to introduce participants to the Stata software. No prior experience with Stata is required. The sections are:\n\nSection 1: The Basics of Stata – Interacting with Stata. (10/19 2-3:30)\nSection 2: Working with Data Sets – Importing\, opening\, and saving data sets. (10/21 2-3:30)\nSection 3: Data Management – The basics of maintaining and exploring a data set. (10/23 2-3:30)\nSection 4: Data Manipulation – Creating and modifying variables and other ways of manipulating your data. (10/26 2-3:30)\n\nYou do not need to attend all sessions; however\, the sessions build on each other and it will be assumed you are familiar with the material in earlier sessions. The workshop materials can be found at https://cscar.github.io/workshop-stata-intro/ for review.\n\nIt is strongly encouraged that you have a current version of Stata (version 16) available on your local computer\, though not required. You may access Stata through midesktop (https://midesktop.umich.edu/) if needed. If you have an older version of Stata\, some material may not work on your system.
URL:https://micde.umich.edu/event/stata-data-management/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201021T140000
DTEND;TZID=America/Detroit:20201021T153000
DTSTAMP:20260605T023629
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000413-1603288800-1603294200@micde.umich.edu
SUMMARY:Stata: Working with Data Sets
DESCRIPTION:This is a series of workshops designed to introduce participants to the Stata software. No prior experience with Stata is required. The sections are:\n\nSection 1: The Basics of Stata – Interacting with Stata. (10/19 2-3:30)\nSection 2: Working with Data Sets – Importing\, opening\, and saving data sets. (10/21 2-3:30)\nSection 3: Data Management – The basics of maintaining and exploring a data set. (10/23 2-3:30)\nSection 4: Data Manipulation – Creating and modifying variables and other ways of manipulating your data. (10/26 2-3:30)\n\nYou do not need to attend all sessions; however\, the sessions build on each other and it will be assumed you are familiar with the material in earlier sessions. The workshop materials can be found at https://cscar.github.io/workshop-stata-intro/ for review.\n\nIt is strongly encouraged that you have a current version of Stata (version 16) available on your local computer\, though not required. You may access Stata through midesktop (https://midesktop.umich.edu/) if needed. If you have an older version of Stata\, some material may not work on your system.
URL:https://micde.umich.edu/event/stata-working-with-data-sets/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201021T130000
DTEND;TZID=America/Detroit:20201021T170000
DTSTAMP:20260605T023629
CREATED:20230905T171256Z
LAST-MODIFIED:20230905T171256Z
UID:10000419-1603285200-1603299600@micde.umich.edu
SUMMARY:Mixed Models with R
DESCRIPTION:Mixed models are an extremely useful modeling tool for situations in which there is some dependency among observations in the data\, where the correlation typically arises from the observations being clustered in some way. For example\, it is quite common to have data in which we have repeated measurements for the units of observation\, or in which the units of observation are otherwise clustered (e.g. students within school\, cities within geographic region). While there are different ways to approach such a situation\, mixed models are a very common and powerful tool to do so.  In addition\, they have ties to other statistical approaches that further expand their applicability. \nThe goal of this workshop is primarily to provide a sense of when one would use mixed models and how to incorporate a variety of standard techniques.  It is very applied in nature\, and only assumes a basic understanding of standard regression models (and use of R for such models). \nLink: https://m-clark.github.io/mixed-models-with-R/
URL:https://micde.umich.edu/event/mixed-models-with-r-3/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201020T150000
DTEND;TZID=America/Detroit:20201020T163000
DTSTAMP:20260605T023629
CREATED:20230905T171256Z
LAST-MODIFIED:20230905T171256Z
UID:10000421-1603206000-1603211400@micde.umich.edu
SUMMARY:QGIS-II
DESCRIPTION:This workshop will provide a hands-on exposure to geometrical operations on vector data in QGIS\, and will also cover a few basic operations that combine raster and vector data. The workshop will be especially useful for participants with some exposure to GIS (similar to the material covered in the previous workshop). \nThis workshop is virtual. The presenter will be in touch with more information after you register.
URL:https://micde.umich.edu/event/gis-ii/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201020T150000
DTEND;TZID=America/Detroit:20201020T160000
DTSTAMP:20260605T023629
CREATED:20230905T171253Z
LAST-MODIFIED:20230905T171253Z
UID:10000402-1603206000-1603209600@micde.umich.edu
SUMMARY:MICDE Seminar: Grace Gu\, Assistant Professor\, Mechanical Engineering\, University of California- Berkeley
DESCRIPTION:About Grace Gu: Grace X. Gu is an Assistant Professor of Mechanical Engineering at the University of California\, Berkeley. She received her Ph.D. and MS in Mechanical Engineering from the Massachusetts Institute of Technology and her BS in Mechanical Engineering from the University of Michigan\, Ann Arbor. Her current research focuses on creating new materials with superior properties for mechanical\, biological\, and energy applications using multiphysics modeling\, artificial intelligence\, and high-throughput computing\, as well as developing intelligent additive manufacturing technologies to realize complex material designs previously impossible. Gu is the recipient of several awards\, including the 3M Non-Tenured Faculty Award\, MIT Tech Review Innovators Under 35\, Johnson & Johnson Women in STEM2D Scholars Award\, Royal Society of Chemistry Materials Horizons Outstanding Paper Prize\, and SME Outstanding Young Manufacturing Engineer Award. \n  \n\nMETAMATERIALS DESIGN AND MANUFACTURING: LEARNING FROM BIOLOGY AND ARTIFICIAL INTELLIGENCE\nAfter billions of years of evolution\, it is no surprise that biological materials are treated as an invaluable source of inspiration in the search for new materials. Additionally\, developments in computation spurred the fourth paradigm of materials discovery and design using artificial intelligence. Our research aims to advance design and manufacturing processes to create the next generation of high-performance engineering and biological materials by harnessing techniques integrating artificial intelligence\, multiphysics modeling\, and multiscale experimental characterization. This work combines computational methods and algorithms to investigate design principles and mechanisms embedded in materials with superior properties\, including bioinspired materials. Additionally\, we develop and implement deep learning algorithms to detect and resolve problems in current additive manufacturing technologies\, allowing for automated quality assessment and the creation of functional and reliable structural materials. These advances will find applications in robotic devices\, energy storage technologies\, orthopedic implants\, among many others. In the future\, this algorithmically driven approach will enable materials-by-design of complex architectures\, opening up new avenues of research on advanced materials with specific functions and desired properties. \n\nThe MICDE Fall 2020 and Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nTo view the recording for this event\, please complete this form and a link will be sent to you. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-grace-gu-assistant-professor-mechanical-engineering-university-of-california-berkeley/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Grace-Gu.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201020T113000
DTEND;TZID=America/Detroit:20201020T130000
DTSTAMP:20260605T023629
CREATED:20230905T171253Z
LAST-MODIFIED:20230905T171253Z
UID:10000405-1603193400-1603198800@micde.umich.edu
SUMMARY:LSA Complex Systems / MICDE / MIDAS Seminar: Marissa Renardy\, Research Fellow\, Microbiology & Immunology\, University of Michigan
DESCRIPTION:Predicting the second wave of COVID-19 in Washtenaw County\, MI\nAbstract: In this work\, we study and predict the spread of COVID-19 in Washtenaw County\, MI through applying a discrete and stochastic network-based modeling framework. In this framework\, we construct contact networks based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households\, workplaces\, schools\, and group quarters (such as prisons or long term care facilities). In addition\, we assign casual contacts to each individual at random. Using this framework\, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases of COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular\, we consider the effects of 1) different timings for reopening\, and 2) different levels of workplace vs. casual contact re-engagement. Through simulations and sensitivity analyses\, we explore mechanisms driving the magnitude and timing of a second wave of infections upon re-opening. \nThis work is based on Dr. Renardy’s paper in press in the Journal of Theoretical Biology with coauthors:\nMarisa Eisenberg\, UM Complex Systems & Math (LSA) and Epidemiology (Public Health)\nDenise Kirschner\, UM Department of Microbiology & Immunology (Medical School) \nRegistration is not required for this event\, you may join the seminar via this link. \nThe recording of this webinar will be available for viewing soon! \nThis seminar is hosted by the LSA Center for the Study of Complex Systems\, and co-sponsored by the Michigan Institute for Computational Discovery & Engineering (MICDE) and the Michigan Institute for Data Science (MIDAS).
URL:https://micde.umich.edu/event/lsa-complex-systems-micde-midas-seminar-marissa-renardy-research-fellow-microbiology-immunology-university-of-michigan/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Marissa-Renardy.png
END:VEVENT
END:VCALENDAR