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DTSTART;TZID=America/Detroit:20210305T153000
DTEND;TZID=America/Detroit:20210305T170000
DTSTAMP:20260605T000914
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SUMMARY:GIS Fundamentals – V (Spatial Database - PostGIS)
DESCRIPTION:This is the fifth 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 somewhat self-contained. The focus is on conceptual details that can provide sufficient preparation for applications\, but we will also touch upon the technical aspects. \n\nIn this workshop we will cover the basic concepts of spatial databases and learn about setting up and using PostGIS\, an open source spatial database built on top of PostgreSQL\, along with R for vector data analysis. We will also touch upon topics such as spatial indexing\, query processing and the capabilities of PostGIS for other data models such as the network and raster data model. This is a hands-on workshop and the instructor will use a Mac machine. If you intend to use a Windows or Linux machine please get in touch with the instructor before the workshop at manishve@umich.edu.
URL:https://micde.umich.edu/event/gis-fundamentals-v-spatial-database-postgis/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210304T160000
DTEND;TZID=America/Detroit:20210304T170000
DTSTAMP:20260605T000914
CREATED:20230905T171259Z
LAST-MODIFIED:20260403T173300Z
UID:10000458-1614873600-1614877200@micde.umich.edu
SUMMARY:PhD Seminar: K G & Ryan Sandberg
DESCRIPTION:K G\, PSYCHOLOGY & SCIENTIFIC COMPUTING \nBio: K is a 4th year PhD candidate in Psychology and Scientific Computing. He has a Bachelors and a Masters degree in Biomedical Engineering and a Masters in Psychology. He works in the multisensory perception lab with Dr. David Brang and studies how multisensory integration occurs in the human brain and their mechanisms. \nEFFECTS OF VISUAL SPEECH ON AUDITORY SPEECH PERCEPTION: For quite some time now\, the notion of different regions in the brain being highly interconnected instead of being segregated into modules has been widely discussed. There are numerous studies that provide evidence for such an effect where distinct regions in the brain responsible for different functionalities work together to create a unified sense of reality. A case in point would be audio-visual integration\, where a person’s auditory stimuli/input is modulated by visual stimuli. One such example is the McGurk effect where the auditory component of one sound\, paired with the visual component of another sound leads to the perception of a third sound. How does this effect happen and what are the ways in which the brain handles integration of these different senses? My research explores questions such as whether the brain integrates information from two different senses in a third\, unrelated region of the brain or whether the sense of integration is just an illusion created by the modulatory effect of one sense on another. In this talk\, I would provide evidence indicating a modulatory effect of visual stimuli on auditory speech perception. Results from complimentary data obtained using two different imaging modalities including intracranial electrocortocographic recordings and functional magnetic resonance imaging would be discussed. \n  \nRYAN SANDBERG\, GRADUATE STUDENT\, APPLIED AND INTERDISCIPLINARY MATHEMATICS & SCIENTIFIC COMPUTING \nBio: I work with Robert Krasny in math and Alec Thomas in NERS on numerical methods in plasma physics\, incorporating tree codes and particle methods in plasma simulation. I also study plasma-based electron and photon acceleration. \nFARRSIGHT: A FORWARD ADAPTIVELY REFINED AND REGULARIZED SEMI-LAGRANGIAN INTEGRAL GPU- AND HEIRARCHICAL TREECODE-ACCELERATED METHOD FOR THE VLASOV-POISSON SYSTEM: We present a new forward semi-Lagrangian particle method for the Vlasov-Poisson (VP) system. Recently developed methods for the VP system include deformable particles and high-order or discontinuous-Galerkin Eulerian methods. In contrast to these\, we do not use any operator splitting and obtain the electric field by summing regularized pairwise particle interactions using a GPU-accelerated tree-code. We remesh and use adaptive mesh refinement to maintain an efficient representation of phase space. We benchmark on several standard test cases including Landau damping and the two-stream instability. We also compare the multi-threaded and single-GPU performance of the method. \n\nThis event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nRegister via Zoom to immediately receive login details for this event. Note: You may register and join after the event has started. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/phd-seminar-kg-ryan-sandberg/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE PhD Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210301T130000
DTEND;TZID=America/Detroit:20210301T140000
DTSTAMP:20260605T000914
CREATED:20230905T171259Z
LAST-MODIFIED:20230905T171259Z
UID:10000450-1614603600-1614607200@micde.umich.edu
SUMMARY:MICDE Seminar: Santo Fortunato\, Director of the Indiana University Network Science Institute (IUNI)\, Professor\, School of Informatics\, Computing\, and Engineering (SICE)\, Indiana University at Bloomington
DESCRIPTION:About Dr. Fortunato: Santo Fortunato is the Director of the Indiana University Network Science Institute (IUNI) and a faculty at Luddy School of Informatics\, Computing and Engineering. Previously he was professor of complex systems at the Department of Computer Science of Aalto University\, Finland. Prof. Fortunato got his PhD in Theoretical Particle Physics at the University of Bielefeld In Germany. He then moved to the field of complex systems\, via a postdoctoral appointment at Luddy School of Informatics\, Computing and Engineering of Indiana University. His current focus areas are network science\, especially community detection in graphs\, computational social science\, science of science\, climate change. His research has been published in leading journals\, including Nature\, Science\, PNAS\, Physical Review Letters\, Reviews of Modern Physics\, Physics Reports and has collected over 33\,000 citations (Google Scholar). His review article Community detection in graphs (Physics Reports 486\, 75-174\, 2010) is one of the best known and most cited papers in network science. He received the Young Scientist Award for Socio- and Econophysics 2011\, a prize given by the German Physical Society\, for his outstanding contributions to the physics of social systems. He is the Founding Chair of the International Conference on Computational Social Science (IC2S2) and Chair of Networks 2021\, the first merger of the NetSci and the Sunbelt conferences\, possibly the largest ever event in network science. \nCOMMUNITY DETECTION IN NETWORKS: Complex systems typically display a modular structure\, as modules are easier to assemble than the individual units of the system\, and more resilient to failures. In the network representation of complex systems\, modules\, or communities\, appear as subgraphs whose nodes have an appreciably larger probability to get connected to each other than to other nodes of the network. In this talk I will discuss three main issues in this area. I will address the limits of the most popular class of clustering algorithms\, those based on the optimization of a global quality function\, like modularity maximization. Testing algorithms is probably the single most important issue of network community detection\, as it implicitly involves the concept of community\, which is ill-defined. I will discuss the importance of using realistic benchmark graphs with built-in community structure. Finally\, I will introduce an increasingly popular post-processing technique that allows to “average” the results of stochastic clustering algorithms\, improving their quality: consensus clustering. \n\nWatch the full webinar recording. \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. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-santo-fortunato-director-of-the-indiana-university-network-science-institute-iuni-professor-school-of-informatics-computing-and-engineering-sice-indiana-university-at-blooming/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2021/01/Santo-Fortunato.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210226T140000
DTEND;TZID=America/Detroit:20210226T153000
DTSTAMP:20260605T000914
CREATED:20230905T171259Z
LAST-MODIFIED:20230905T171259Z
UID:10000435-1614348000-1614353400@micde.umich.edu
SUMMARY:Introduction to 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 and working with data sets. (2/22 2-3:30)\nSection 2: Data Management – The basics of maintaining and exploring a data set. (2/24 2-3:30)\nSection 3: Data Manipulation – Creating and modifying variables and other ways of manipulating your data. (2/26 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 Stata available on your local computer\, though not required. You may access Stata through midesktop (https://midesktop.umich.edu/) if needed.
URL:https://micde.umich.edu/event/introduction-to-stata-data-manipulation/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210225T160000
DTEND;TZID=America/Detroit:20210225T170000
DTSTAMP:20260605T000914
CREATED:20230905T171259Z
LAST-MODIFIED:20230905T171259Z
UID:10000453-1614268800-1614272400@micde.umich.edu
SUMMARY:Ph.D. Seminar: Anil Yildirim & Jiale Tan
DESCRIPTION:ANIL YILDIRIM\, GRADUATE STUDENT\, AEROSPACE ENGINEERING & SCIENTIFIC COMPUTING \nBio: Anil Yildirim is a PhD candidate in Aerospace Engineering and Scientific Computing. His research focuses on the development and application of robust computational tools in the context of multidisciplinary design optimization for aircraft configurations. \nROBUST AND HIGH-PERFORMANCE TOOLS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION: The development of future sustainable aircraft heavily relies on the design and integration of advanced propulsion systems. However\, the design of these systems are challenging due to the tightly coupled interactions between the aerodynamic and the propulsion disciplines. My research focuses on enabling these advanced technologies using aeropropulsive design optimization\, in which the aerodynamic and propulsion system designs are optimized in a coupled manner. In this process\, I use multiple robust and high-performance computational tools including the computational fluid dynamics (CFD) solver we have been developing in the MDO Lab at the University of Michigan. In this talk\, I will cover some recent advancements in the field of CFD-based aeropropulsive design optimization and the computational methodologies we have been using for this work. \n  \nJIALE TAN\, GRADUATE STUDENT\, EPIDEMIOLOGY & SCIENTIFIC COMPUTING \nBio: Jiale is a second year Phd student working with Prof. Rafael Meza in Epidemiology. His interest is to apply computational skills to public health challenges so that he can develop and apply modeling techniques for infectious and noninfectious diseases\, including for viral infections like HIV and HCV\, and eventually use them for modeling non-communicable diseases that disproportionately affect global health like cancer. \nMARKOV MULTISTATE TRANSITION MODEL ON ELECTRONIC NICOTINE DELIVERY SYSTEMS AND TRADITIONAL CIGARETTES: Electronic nicotine delivery systems (ENDS) have dramatically changed the landscape of tobacco products patterns in the USA since 2011. The impact of ENDS use on traditional cigarettes smoking remains a topic of considerable debate. A Markov multistate transition model was used to estimate transition rates (Hazard rate) between ENDS and cigarette use states (25 use states); never user\, non-current experimental user\, non-current regular user\, current experimental user\, and current regular user for each product. A 25×25 transition matrix was generated from this model. Parallel computations using 150 processors was used to estimate the transition rates. The Population Assessment of Tobacco and Health study\, which includes longitudinal data from 11\,475 youth of ages 12 to 24 years from 2013-2018 was used to calibrate the model. The hazard estimates show the patterns of ENDS and cigarette use experimentation and transition to regular use. Next steps will assess the impact of different sociodemographic covariates (age\, sex\, race\, education\, household income) on the estimated transition rates. \n\nThis event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis webinar was not recorded for public distribution. \nQuestions? Email MICDE-events@umich.edu \n\nAdditional research image from Anil Yildirim.
URL:https://micde.umich.edu/event/ph-d-seminar-anil-yildirim-jiale-tan/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,hpc-events,MICDE PhD Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210225T160000
DTEND;TZID=America/Detroit:20210225T170000
DTSTAMP:20260605T000914
CREATED:20230905T171259Z
LAST-MODIFIED:20230905T171259Z
UID:10000455-1614268800-1614272400@micde.umich.edu
SUMMARY:CoreLogic property data
DESCRIPTION:The University of Michigan library system has licensed a large data set containing real estate transactions\, deeds\, and property tax records for the United States.  The data were collected by the commercial vendor CoreLogic\, and our license allows UM researchers to use the data for research purposes.  These data are of potential interest to researchers in many fields\, as they capture spatial and temporal real estate market conditions\, taxing practices\, and the physical states of millions of residential structures in the US.\n\n \nIn this workshop\, participants will learn to create geographical subsets of the data\, seamlessly integrate it in workflow\, and see examples of research questions where the data can be useful. Participants should know Python and R.
URL:https://micde.umich.edu/event/corelogic-property-data-2/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210224T140000
DTEND;TZID=America/Detroit:20210224T170000
DTSTAMP:20260605T000914
CREATED:20230905T171259Z
LAST-MODIFIED:20230905T171259Z
UID:10000016-1614175200-1614186000@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-3-2/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210224T140000
DTEND;TZID=America/Detroit:20210224T170000
DTSTAMP:20260605T000914
CREATED:20230905T171259Z
LAST-MODIFIED:20230905T171259Z
UID:10000454-1614175200-1614186000@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. 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-4/
LOCATION:Your Desktop
CATEGORIES:Featured Events,Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210224T140000
DTEND;TZID=America/Detroit:20210224T153000
DTSTAMP:20260605T000914
CREATED:20230905T171259Z
LAST-MODIFIED:20230905T171259Z
UID:10000436-1614175200-1614180600@micde.umich.edu
SUMMARY:Introduction to 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 and working with data sets. (2/22 2-3:30)\nSection 2: Data Management – The basics of maintaining and exploring a data set. (2/24 2-3:30)\nSection 3: Data Manipulation – Creating and modifying variables and other ways of manipulating your data. (2/26 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 Stata available on your local computer\, though not required. You may access Stata through midesktop (https://midesktop.umich.edu/) if needed.
URL:https://micde.umich.edu/event/introduction-to-stata-data-management/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210224T130000
DTEND;TZID=America/Detroit:20210224T150000
DTSTAMP:20260605T000914
CREATED:20230905T171300Z
LAST-MODIFIED:20230905T171300Z
UID:10000438-1614171600-1614178800@micde.umich.edu
SUMMARY:Software Development For Research: Best Practices for Coding Styles
DESCRIPTION:Software development and computer programming is increasingly a major part of scientific research. Having a consistent coding style and following basic best practices used in the industry can help make your code easier to read and manage\, both internally in your teams and for public code projects available to other researchers. This workshop will cover some general guidelines and suggestions to clean up your coding style. Attendees will learn helpful tips for computer coding and how to make their code readable to other collaborators. \nThis is part of a series of workshops focused on software and coding from a research perspective.
URL:https://micde.umich.edu/event/software-development-for-research-best-practices-for-coding-styles/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210223T100000
DTEND;TZID=America/Detroit:20210223T120000
DTSTAMP:20260605T000914
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000430-1614074400-1614081600@micde.umich.edu
SUMMARY:Image Segmentation using Deep Learning with FastAI
DESCRIPTION:Like many image processing problems\, deep learning has brought many effective solutions to the task of image segmentation. This workshop will introduce you to the methods used in image segmentation\, demonstrate how to prepare your own segmentation masks using Matlab\, and guide you through performing 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 Zoom. We will run the code using Google Colab (with free-to-use GPUs)\, which requires a Google account.
URL:https://micde.umich.edu/event/image-segmentation-using-deep-learning-with-fastai-2/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210222T140000
DTEND;TZID=America/Detroit:20210222T153000
DTSTAMP:20260605T000914
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000434-1614002400-1614007800@micde.umich.edu
SUMMARY:Introduction to Stata: The Basics of Stata
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: \nSection 1: The Basics of Stata – Interacting with Stata and working with data sets. (2/22 2-3:30)\nSection 2: Data Management – The basics of maintaining and exploring a data set. (2/24 2-3:30)\nSection 3: Data Manipulation – Creating and modifying variables and other ways of manipulating your data. (2/26 2-3:30) \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. \nIt is strongly encouraged that you have Stata available on your local computer\, though not required. You may access Stata through midesktop (https://midesktop.umich.edu/) if needed.
URL:https://micde.umich.edu/event/introduction-to-stata-the-basics-of-stata/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210218T160000
DTEND;TZID=America/Detroit:20210218T170000
DTSTAMP:20260605T000914
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000452-1613664000-1613667600@micde.umich.edu
SUMMARY:Ph.D Seminar: Matthew Duschenes & Yi Zhu
DESCRIPTION:MATTHEW DUSCHENES\, GRADUATE STUDENT\, APPLIED PHYSICS & SCIENTIFIC COMPUTING \nBio: I am in my third year of the Applied Physics & Scientific Computing Ph.D. programs\, after completing a master’s in theoretical physics in my home country of Canada. As a member of Dr. Krishna Garikipati’s Computational Physics group\, I am currently working on data driven modelling and am collaborating with several groups on applying these graph theoretic approaches to various systems of interest. \nGRAPH THEORETIC APPROACHES FOR PHYSICAL SYSTEMS: Numerical analyses of physical systems are conventionally performed using direct numerical simulations\, that have proven highly successful\, yielding high fidelity solutions to very high dimensional problems\, such as boundary value problems with upwards of tens of millions of degrees of freedom. However\, there is always a balance to be met between the desire for higher accuracy and additional physics to be modeled\, and the complexity\, interpret-ability and ease of representation of such solutions. To aid in this dilemma\, I will be introducing a novel graph theoretic approach\, allowing for lower dimensional\, reduced order models to be produced\, given small amounts of high fidelity data. In this talk I will explain how such an approach allows for an intuitive representation of the states of a systems\, and how it is possible to use a non-local calculus\, allowing for rigorous operators and equations to be defined on the graph. I will then be discussing some implementation details\, and convey the generality\, validity\, and future applications of this framework through some example results from collaborations. \nYI ZHU\, GRADUATE STUDENT\, CIVIL AND ENVIRONMENTAL ENGINEERING & SCIENTIFIC COMPUTING \n \nBio: Yi is a 3rd year PhD candidate in Civil and Environmental Engineering & Scientific Computation. His research focuses on simulation\, design\, and fabrication of active origami systems for engineering devices\, and is particularly focused on micro-scale shape morphing systems inspired by origami. \nSIMULATION AND DESIGN OF MICRO-ORIGAMI SYSTEMS: In this talk\, we will introduce some recent advancement in the simulation and the design of micro-origami systems. We will discuss the micro-origami structures we fabricated and the rapid simulation framework we developed to capture the behaviors of these active origami. We will focus on the simulation framework and demonstrate how we can capture the thermo-mechanically coupled folding behavior and contacts between origami panels effectively and rapidly. Finally\, we will introduce some ongoing work on extracting origami design principle with interpretable machine learning\, which demonstrates how we can use the simulation framework to create better origami design. \n  \n  \n\nThis event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/ph-d-seminar-matthew-duschenes-yi-zhu/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE PhD Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210216T150000
DTEND;TZID=America/Detroit:20210216T160000
DTSTAMP:20260605T000914
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000428-1613487600-1613491200@micde.umich.edu
SUMMARY:MICDE Seminar: Emma Lejeune\, Assistant Professor\, Mechanical Engineering\, Boston University
DESCRIPTION:Bio: Emma Lejeune is an Assistant Professor in the Mechanical Engineering Department at Boston University. She received her PhD from Stanford University in September 2018\, and was a Peter O’Donnell\, Jr. postdoctoral research fellow at the Oden Institute at the University of Texas at Austin until 2020 when she joined the faculty at BU. At BU\, Emma has received the David R. Dalton Career Development Professorship\, a Computational Science and Engineering Junior Faculty Fellowship\, and the Haythornthwaite Research Initiation Grant from the ASME Applied Mechanics Division. Current areas of research involve integrating data-driven and physics based computational models\, and characterizing and predicting the mechanical behavior of heterogeneous materials and biological systems. \nMODELING HETEROGENEOUS MATERIALS: BENCHMARK DATASETS\, METAMODELS\, AND EXPERIMENTAL CHARACTERIZATION: \nBiological systems are spatially heterogeneous across scales. To effectively model biological materials we need new tools to quantify and capture this heterogeneity. In this talk\, we will first discuss our recent work on simulating spatially heterogeneous materials. Specifically\, we will discuss our recent work in developing and exploring benchmark datasets of spatially heterogeneous materials simulated with the finite element method. These datasets are useful primarily for constructing metamodels\, or computationally cheap models of models\, that map defined model inputs to defined model outputs. By nature\, a given metamodel will be tailored to a specific dataset. However\, the most pragmatic metamodel type and structure will often be general to larger classes of problems. At present\, the most pragmatic metamodel selection for predicting the mechanical behavior of spatially heterogeneous materials — specifically simulations of heterogenous materials — has not been thoroughly explored. Drawing inspiration from the benchmark datasets available to the computer vision research community\, we introduce a benchmark data set (Mechanical MNIST https://open.bu.edu/handle/2144/39371) for constructing metamodels of heterogeneous material undergoing large deformation. We then show a few examples of problems that we have explored thus far with this dataset. Looking forward\, we anticipate that disseminating benchmark datasets will enable the broader community of researchers to develop improved metamodeling techniques for capturing the behavior of spatially heterogeneous materials that will surpass the baseline performance that we show here. Finally\, to conclude the talk\, we will change gears and briefly discuss some of our recent work on creating new tools for characterizing cell behavior using concepts from kinematics and spatial statistics. Looking forward\, we are interested in the natural synergy between advances in methods for both simulating and characterizing heterogeneous materials. \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. \nDr. Lejeune will be hosted by Professor Krishna Garikipati\, MICDE Director. \nWatch the full webinar. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-emma-lejeune-assistant-professor-mechanical-engineering-boston-university/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/11/Emma-Lejeune.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210212T150000
DTEND;TZID=America/Detroit:20210212T163000
DTSTAMP:20260605T000914
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000448-1613142000-1613147400@micde.umich.edu
SUMMARY:GIS Fundamentals – IV (Digitization)
DESCRIPTION:This is the fourth 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. 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. \nIn many fields old paper maps and images provide historical and time series information and need to be digitized with spatially explicit coordinates. In this workshop we will develop a basic understanding of how to efficiently digitize paper maps and images that contain spatial information. We will use ArcGIS and R for the hands-on part.
URL:https://micde.umich.edu/event/gis-fundamentals-iv-digitization/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210211T160000
DTEND;TZID=America/Detroit:20210211T163000
DTSTAMP:20260605T000914
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000451-1613059200-1613061000@micde.umich.edu
SUMMARY:Ph.D Seminar: Saibal De\, Applied and Interdisciplinary Mathematics & Scientific Computing
DESCRIPTION:Bio: Saibal De is a 5th year PhD candidate in Applied and Interdisciplinary Mathematics. His research involves using high-performance computing and novel algorithms for accelerating physics-based simulation frameworks\, and developing faithful reduced-order models of black-box high-fidelity simulations. \nTENSOR METHODS FOR DATA COMPRESSION: With the advancement of computing software and hardware\, physics-based simulations have gained notoriety in many scientific and industrial applications due to their highly accurate prediction capabilities. However\, in addition to being computationally expensive\, even a single of these high-fidelity simulations produce massive amounts of data. Storing and processing all these data thus requires novel approaches. In this talk\, I will present how we can use tensor factorization methods for compressing scientific data\, leading to dramatic savings in disk-space usage. Towards the end of the talk\, I’ll also touch upon how we can potentially construct reduced-order models out of these compressed datasets. \n\nThis event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/ph-d-seminar-saibal-de-applied-and-interdisciplinary-mathematics-scientific-computing/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE PhD Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/Headshot-Saibal-De.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210210T100000
DTEND;TZID=America/Detroit:20210210T120000
DTSTAMP:20260605T000914
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000444-1612951200-1612958400@micde.umich.edu
SUMMARY:Introduction to SPSS: Basic Statistical Analysis
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-basic-statistical-analysis/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210209T160000
DTEND;TZID=America/Detroit:20210209T170000
DTSTAMP:20260605T000914
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000408-1612886400-1612890000@micde.umich.edu
SUMMARY:MICDE / Mechanical Engineering Seminar: Ceila Reina\, Assistant Professor\, Mechanical Engineering and Applied Mechanics\, University of Pennsylvania
DESCRIPTION:Bio:  Celia Reina is the William K. Gemmill Term Assistant Professor in Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. She joined in 2014 after holding the Lawrence Postdoctoral Fellowship at Lawrence Livermore National Laboratory and the HCM postdoctoral Fellowship at the Hausdorff Center of Mathematics in Bonn\, Germany. Dr. Reina received her PhD from the California Institute of Technology in Aerospace Engineering in 2011\, under the supervision of Prof. Michael Ortiz\, following a B.S. in Mechanical Engineering from the University of Seville in Spain\, and a Master in Structural Dynamics from Ecole Centrale Paris in France. She is the 2017 recipient of the Eshelby Mechanics Award for Young Faculty\, she is a member of the TTA on Nanotechnology and Lower Scale Phenomena at the USACM\, and she currently serves as the recording secretary for the Applied Mechanics Division of the ASME. \nCONTINUUM MECHANICS OF NON-EQUILIBRIUM PHENOMENA: A JOURNEY THROUGH SPACE AND TIME SCALES:  The fascinating diversity of material behavior at the macroscopic scale can only emerge from the underlying atomistic or particle behavior. Yet\, the direct connection between these two scales remains an extremely challenging quest\, particularly in the context of non-equilibrium phenomena. In this talk\, we will discuss several advances in this direction\, in the context of plasticity\, thermoelasticity\, diffusion and viscous dissipation. In all these cases\, the importance of fluctuations in the effective response will become apparent. More precisely\, these will provide crucial information for the material description and evolution at the continuum scale\, where the behavior is modeled as deterministic and free of fluctuations. \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. \nThis event will be a joint seminar with the University of Michigan College of Engineering’s Mechanical Engineering department. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-mechanical-engineering-seminar-ceila-reina-assistant-professor-mechanical-engineering-and-applied-mechanics-university-of-pennsylvania/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Celia-Reina.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210204T110000
DTEND;TZID=America/Detroit:20210204T120000
DTSTAMP:20260605T000914
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000407-1612436400-1612440000@micde.umich.edu
SUMMARY:MICDE / MIDAS Seminar: Ivo Dinov\, Professor\, Nursing\, Computational Medicine & Bioinformatics
DESCRIPTION:Bio: Dr. Ivo D. Dinov directs the Statistics Online Computational Resource (SOCR)\, co-directs the multi-institutional Probability Distributome Project\, and is an associate director for education of the Michigan Institute for Data Science (MIDAS). \nDr. Dinov is an expert in mathematical modeling\, statistical analysis\, computational processing and visualization of Big Data. He is involved in longitudinal morphometric studies of human development (e.g.\, Autism\, Schizophrenia)\, maturation (e.g.\, depression\, pain) and aging (e.g.\, Alzheimer’s and Parkinson’s diseases). Dr. Dinov is developing\, validating and disseminating novel technology-enhanced pedagogical approaches for scientific education and active learning. \nDATA SCIENCE\, TIME COMPLEXITY\, AND SPACEKIME ANALYTICS \nMany observable processes demand managing\, harmonizing\, modeling\, analyzing\, interpreting\, and visualizing of large and complex information. There is a substantial need to develop\, validate\, productize\, and support novel mathematical techniques\, advanced statistical computing algorithms\, transdisciplinary tools\, and effective artificial intelligence applications. Spacekime analytics is a new technique for modeling high-dimensional longitudinal data. This approach relies on extending the notions of time\, events\, particles\, and wavefunctions to complex-time (kime)\, complex-events (kevents)\, data\, and inference-functions. We will illustrate how the kime-magnitude (longitudinal time order) and kime-direction (phase) affect the subsequent predictive analytics and the induced scientific inference. \nThe mathematical foundation of spacekime calculus reveal various statistical implications including inferential uncertainty and a Bayesian formulation of spacekime analytics. Complexifying time allows the lifting of all commonly observed processes from the classical 4D Minkowski spacetime to a 5D spacekime manifold\, where a number of interesting mathematical problems arise. Direct data science applications of spacekime analytics will be demonstrated using simulated data and clinical observations (e.g.\, structural and functional MRI). \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 recorded webinar. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-ivo-dinov-professor-nursing-and-computational-medicine-bioinformatics-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/Ivo-Dinov.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210203T153000
DTEND;TZID=America/Detroit:20210203T170000
DTSTAMP:20260605T000914
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000447-1612366200-1612371600@micde.umich.edu
SUMMARY:GIS Fundamentals – III (Geocoding)
DESCRIPTION:This is the third 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. 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. \n\nGeocoding (or sometimes reverse geocoding) is often a very first step in many geospatial analyses. There are many options available for geocoding with different degrees of accuracy. A basic understanding of the process helps you in choosing the best option. We will use R and ArcGIS for exercises
URL:https://micde.umich.edu/event/gis-fundamentals-iii-geocoding/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210203T100000
DTEND;TZID=America/Detroit:20210203T120000
DTSTAMP:20260605T000914
CREATED:20230905T171258Z
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:20260605T000914
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:20260605T000914
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/
LOCATION:MI
CATEGORIES:Featured Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210127T100000
DTEND;TZID=America/Detroit:20210127T120000
DTSTAMP:20260605T000914
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:20260605T000914
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:20260605T000914
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:20260605T000914
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:20260605T000914
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:20260605T000914
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:20260605T000914
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
END:VCALENDAR