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X-WR-CALDESC:Events for Michigan Institute for Computational Discovery and Engineering
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220309T090000
DTEND;TZID=America/Detroit:20220309T120000
DTSTAMP:20220110T231814Z
CREATED:20220110T231814Z
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SUMMARY:Introduction to the Linux Command Line
DESCRIPTION:OVERVIEW\nThis course will familiarize the student with the basics of accessing and interacting with Linux computers using the GNU/Linux operating system’s Bash shell\, also generically referred to as “the command line”. Topics include: a brief overview of Linux\, the Bash shell\, navigating the file system\, basic commands\, shell redirection\, permissions\, processes\, and the command environment. The workshop will also provide a quick introduction to nano a simple text editor that will be used in subsequent workshops to edit files. \n  \nTo register and view more details\, please refer to the linked TTC page
URL:https://micde.umich.edu/event/introduction-to-the-linux-command-line-36-2-2/
LOCATION:Your Desktop
CATEGORIES:Workshops
ORGANIZER;CN="Advanced Research Computing":MAILTO:arc-contact@umich.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220309T100000
DTEND;TZID=America/Detroit:20220309T120000
DTSTAMP:20211217T182335Z
CREATED:20211217T182335Z
LAST-MODIFIED:20211217T182335Z
UID:10000543-1646820000-1646827200@micde.umich.edu
SUMMARY:Software Development For Research: Version Control Principles
DESCRIPTION:Software development and computer programming is increasingly a major part of scientific research. Projects can quickly grow\, and it can be difficult to manage changes\, especially if multiple programmers are editing the same project at once! This workshop will cover Git\, a commonly-used tool for managing coding projects with multiple users\, with features to make or remove edits\, and track information related to those changes. After completing the workshop attendees will have a good understanding of Git workflow and will know how to perform the most common Git operations via the command line. \nThis is part of a series of workshops focused on both technical and soft skills regarding software engineering from a research perspective. \nPlease register at least 48 hours in advance.
URL:https://micde.umich.edu/event/software-development-for-research-version-control-principles-2/
LOCATION:Your Desktop
CATEGORIES:Workshops
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220310T130000
DTEND;TZID=America/Detroit:20220310T160000
DTSTAMP:20220110T224913Z
CREATED:20220110T224913Z
LAST-MODIFIED:20220110T224913Z
UID:10000563-1646917200-1646928000@micde.umich.edu
SUMMARY:Introduction to Research Computing on the Great Lakes Cluster
DESCRIPTION:OVERVIEW\nThis workshop will introduce you to high performance computing on the Great Lakes cluster.  After a brief overview of the components of the cluster and the resources available there\, the main body of the workshop will cover creating batch scripts and the options available to run jobs\, and hands-on experience in submitting\, tracking\, and interpreting the results of submitted jobs. By the end of the workshop\, every participant should have created a submission script\, submitted a job\, tracked its progress\, and collected its output. Additional tools including high-performance data transfer services and interactive use of the cluster will also be covered. \nTo register and view more details\, please refer to the linked TTC page.
URL:https://micde.umich.edu/event/introduction-to-research-computing-on-the-great-lakes-cluster-14-2-2/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220311T090000
DTEND;TZID=America/Detroit:20220311T120000
DTSTAMP:20220110T225518Z
CREATED:20220110T225518Z
LAST-MODIFIED:20220110T225518Z
UID:10000562-1646989200-1647000000@micde.umich.edu
SUMMARY:Advanced Research Computing on the Great Lakes Cluster
DESCRIPTION:OVERVIEW\n\n\nThis workshop will cover some more advanced topics in computing on the U-M Great Lakes Cluster. Topics to be covered include a review of common parallel programming models and basic use of Great Lakes; dependent and array scheduling; workflow scripting using bash; high-throughput computing using launcher; parallel processing in one or more of Python\, R\, and MATLAB; and profiling of parallel code using Allinea Performance Reports and Allinea MAP. \n\nTo register and view more details\, please refer to the linked TTC page.
URL:https://micde.umich.edu/event/advanced-research-computing-on-the-great-lakes-cluster-7-2-2-2-3-2-2/
LOCATION:Your Desktop
CATEGORIES:Great Lakes,High Performance Computing,Workshops
ORGANIZER;CN="Advanced Research Computing":MAILTO:arc-contact@umich.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220314T140000
DTEND;TZID=America/Detroit:20220314T150000
DTSTAMP:20230713T171008Z
CREATED:20220121T172527Z
LAST-MODIFIED:20230713T171008Z
UID:10000554-1647266400-1647270000@micde.umich.edu
SUMMARY:MICDE Seminar: Marta D`Elia\, Principal Member of the Technical Staff\, Sandia National Laboratories
DESCRIPTION:WATCH THE RECORDING HERE. \nBio: Marta D’Elia is a Principal Member of the Technical Staff at Sandia National Laboratories\, where she works since 2014. She’s currently part of the Data Science and Computing group at the California site. She obtained her master degree in Mathematical Engineering at Politecnico of Milano with Prof. Quarteroni and she obtained her Ph.D in Applied Mathematics at Emory University with Prof. Veneziani. There\, she worked on optimal control in CFD for cardiovascular applications. She was a postdoctoral fellow at Florida State University where she worked with Prof. Gunzburger on optimization and control for nonlocal and fractional models. She’s an associate editor of the SIAM Journal on Scientific Computing\, Advances in Continuous and Discrete Models\, Numerical Methods for PDEs\, and the Journal of Peridynamics and Nonlocal Models. Also\, she’s a co-founder of the One Nonlocal World project. Her interests include nonlocal modeling and simulation\, optimization and optimal control\, and scientific machine learning. \nScientific interests: \n\nModeling and Computational aspects of Nonlocal and Fractional equations\,\nScientific Machine Learning\,\nOptimization and Uncertainty Quantification.\n\nDATA-DRIVEN LEARNING OF NONLOCAL MODELS: BRIDGING SCALES WITH NONLOCALITY \nNonlocal models are characterized by integral operators that embed length scales in their definition. As such\, they are preferable to classical partial differential equation models in situations where the dynamics of a system is affected by the small scale behavior\, yet the small scales would require prohibitive computational cost to be treated explicitly. In this sense\, nonlocal models can be considered as coarse-grained\, homogenized models that\, without resolving the small scales\, are still able to accurately capture the system’s global behavior. However\, nonlocal models depend on “kernel functions” that are often hand tuned.\nWe propose to learn optimal kernel functions from high fidelity data by combining machine learning algorithms\, known physics\, and nonlocal theory. This combination guarantees that the resulting model is mathematically well-posed and physically consistent. Furthermore\, by learning the operator rather than a surrogate for the solution\, these models generalize well to settings that are different from the ones used during training. We apply this learning technique to find homogenized nonlocal models for subsurface solute transport solely on the basis of breakthrough curves.\nWe also apply the same kernel-learning technique to design new stable and resolution-independent deep neural networks\, referred to as Nonlocal Kernel Networks (NKN). Stability of NKNs is obtained by imposing constraints derived from the nonlocal vector calculus\, whereas deep training is performed by means of a shallow-to-deep initialization technique. We demonstrate the accuracy and stability of NKNs on PDE-learning and image-classification problems. \n\nThe MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational modeling and machine learning are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the Department of Mechanical Engineering. Dr. D`Elia will be hosted by Dr. Krishna Garikipati\, Professor of Mechanical Engineering\, and of Mathematics. \nThis is a virtual event and will be broadcasted online via Zoom. MICDE students and fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-marta-delia-phd-principal-member-of-the-technical-staff-at-sandia-national-laboratories-california/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/01/Marta-DElia.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220317T100000
DTEND;TZID=America/Detroit:20220317T120000
DTSTAMP:20230217T195820Z
CREATED:20220113T170314Z
LAST-MODIFIED:20230217T195820Z
UID:10000561-1647511200-1647518400@micde.umich.edu
SUMMARY:Processing the CoreLogic Data on Great Lakes using PySpark
DESCRIPTION:OVERVIEW\nThis workshop provides an introduction to processing CoreLogic data using PySpark on the Great Lakes cluster. The CoreLogic dataset contains aggregated data from individual\, parcel-level real estate transactions and financial records. U-M has licensed access to Tax\, Deed\, and Foreclosure data at the parcel level for every county in the United States. We will demonstrate how to request access to the dataset\, and how to quickly get started with processing and running some basic analytics on the data with the user-friendly\, browser-based Open OnDemand tool and the PySpark language on the cluster. \nNote that a Great Lakes account is required for this workshop\, and you must have an account before the start of the workshop in order to participate in the exercises. See below for account request information. \nINSTRUCTORS:\n\nArmand Burks\nResearch Data Scientist Intermediate\nInformation and Technology Services – Advanced Research Computing \nArmand Burks\, Ph.D.\, is a research data scientist intermediate for Advanced Research Computing – Technology Services (ARC-TS)\, a division of Information and Technology Services (ITS). Armand helps researchers with establishing data workflows\, transforming data between different formats\, programming support\, optimizing/parallelizing code\, cloud computing with Hadoop\, and developing custom code (C++\, Java\, Python). He earned a B.S. in computer science from Alabama State University in 2008\, an M.S. in computer science and engineering from Michigan State University in 2010\, and a Ph.D. in computer science from Michigan State University in 2017. \n  \nJule Krüger\nProgram Manager\nCenter for Political Studies\, Institute for Social Research and Information and Technology Services – Advanced Research Computing \n\nJule Krüger\, Ph.D.\, is the ISR Program Manager for Big Data and Data Science\, based within the Center for Political Studies at the Institute for Social Research\, and a member of the Advanced Research Computing Consulting Services.  She has more than 10 years of experience in processing\, analyzing and interpreting data for social science research\, and automating workflows for scalable\, auditable and reproducible analysis. \nMATERIALS \nPrerequisites: Participants will need an active Great Lakes account and login in order to complete hands-on exercises. Some familiarity with PySpark is helpful. \nFor more information on The Great Lakes cluster\, click here https://arc.umich.edu/greatlakes/. \nClick here to fill out an account request form https://arc.umich.edu/login-request \nNote: 3 business days are needed for creation of accounts \nStudents should fill in “Workshop” in the “Advisor” section. \nCampus VPN access is required for off-campus access to Great Lakes but not from on campus. An SSH client\, and Duo will be required during the workshop in order to use Great Lakes.  If you do not have this software already\, please download and install the Cisco AnyConnect VPN software following these instructions: https://its.umich.edu/enterprise/wifi-networks/vpn/getting-started You will need this to be able to use the ssh client. You will need to use the ‘Campus All traffic’ profile in the Cisco client. \nRegister here \nA Zoom link will be provided to the participants the day before the class. Registration is required. \nPlease note\, this session will be recorded.   \n\nIf you have questions about this workshop\, please send an email to the instructors at julianek@umich.edu \n 
URL:https://micde.umich.edu/event/processing-the-corelogic-data-on-great-lakes-using-pyspark/
LOCATION:Your Desktop
CATEGORIES:Great Lakes
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220317T140000
DTEND;TZID=America/Detroit:20220317T160000
DTSTAMP:20220112T185149Z
CREATED:20220112T185149Z
LAST-MODIFIED:20220112T185149Z
UID:10000560-1647525600-1647532800@micde.umich.edu
SUMMARY:Machine Learning on Great Lakes
DESCRIPTION:OVERVIEW\n\n\nThis workshop will go over methods and best practices for running machine learning applications on Great Lakes. We will briefly outline machine learning before stepping through a hands-on example problem to load a project and submit a job to the HPC cluster. Participants are expected to be familiar with Python\, the command line\, and basic Great Lakes functionality (logging in and navigating the directory structure). Participants must create a user account on Great Lakes prior to the workshop and are required to pre-register to gain access to a training account. \nINSTRUCTOR:\nMeghan Dailey\nMachine Learning Specialist\nInformation and Technology Services – Advanced Research Computing \nMeghan Dailey is a machine learning specialist in the Advanced Research Computing (ARC) 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. \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 1:00-2:00 PM for computer setup assistance. \nPlease note\, this session will be recorded.   \nTo register and view more details\, please refer to the linked TTC page. \n\nIf you have questions about this workshop\, please send an email to the instructor at richeym@umich.edu
URL:https://micde.umich.edu/event/https-ttc-iss-lsa-umich-edu-ttc-sessions-machine-learning-for-great-lakes-2/
LOCATION:Your Desktop
CATEGORIES:Data Science,Great Lakes,High Performance Computing,Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220321T160000
DTEND;TZID=America/Detroit:20220321T170000
DTSTAMP:20230713T170841Z
CREATED:20210805T184953Z
LAST-MODIFIED:20230713T170841Z
UID:10000500-1647878400-1647882000@micde.umich.edu
SUMMARY:MICDE / MIDAS Seminar: Yun S. Song\, PhD\, Professor of Computer Science and Statistics\, University of California\, Berkeley
DESCRIPTION:ZOOM LINK\nBio: Professor Yun S. Song is a professor of EECS and Statistics working in mathematical and computational biology. He received his BS degrees in mathematics and physics from MIT\, and a PhD in physics from Stanford University.  Prof. Song’s research centers around computational and mathematical biology. He is generally interested in developing computational tools and statistical methods to facilitate the research of the broad biomedical community\, while also getting deeply involved in data analysis and interpretation.  Prof. Song is also interested in machine learning\, combinatorial optimization\, algorithms\, and Monte Carlo methods. \nRecent honors and awards include NIH Pathway to Independence Award K99/R00 (2006)\, Alfred P. Sloan Research Fellowship (2008)\, Packard Fellowship for Science and Engineering (2008)\, NSF CAREER Award (2009)\, Jim and Donna Gray Faculty Award for Excellence in Undergraduate Teaching (2013)\, Miller Research Professorship (2014)\, Math+X Simons Chair (2015)\, and Chan Zuckerberg Biohub Investigator Award (2017). \n\n  \nTalk Title: Mathematical and machine learning models for predicting protein synthesis and function\n  \nAbstract: Proteins are the workhorses of the cell and are involved in all aspects of cellular processes.  In spite of notable technological advances in protein biology and genomics over the past decade\, it remains an important challenge to unravel how protein synthesis and function are affected by genetic mutations.  In this talk\, I will describe our recent progress in tackling this challenge by leveraging new theoretical results on interacting particle systems and recent advances in natural language processing. \n\nThe MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the Michigan Institute for Data Science (MIDAS). Dr. Song will be hosted by Dr. George Zhang\, Professor of Ecology and Evolutionary Biology. \nThis is a hybrid event and will be held in-person and broadcast online via Zoom. Note: You may register after the event has started. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-midas-seminar-yun-s-song-phd-professor-of-computer-science-and-statistics-university-of-california-berkeley/
LOCATION:West Hall 340
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2021/08/Yun-S.-Song.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220324T140000
DTEND;TZID=America/Detroit:20220324T160000
DTSTAMP:20220308T174743Z
CREATED:20220308T174743Z
LAST-MODIFIED:20220308T174743Z
UID:10000559-1648130400-1648137600@micde.umich.edu
SUMMARY:Reading and discussion group: Spatial Analysis in Social Sciences
DESCRIPTION:This reading group moderated by consultants from CSCAR will focus on spatial analysis especially as practiced in social sciences. We will meet for 1.5 to 2 hours every month on the fourth Thursday and discuss one or two chapters from relevant graduate level textbooks. We will focus on the concepts and applications but will also try to discuss the technical details. The format is open-ended\, and the key objective is to support learning at different knowledge and skill levels. If there is interest\, we will also cover software implementation of techniques in R or Python. We will select reading material that is available via U-M library or freely accessible online. \nThe details for the third meeting are below. \nDate – March 24\, 2022 \nTime – 2:00 pm to 4:00 pm \nReadings – We will discuss the following chapters: \n(1) Chapter 4: Diagnosing Spatial Dependence (from Spatial Analysis for the Social Sciences by David Darmofal) \n(3) Chapter 5: Diagnosing Spatial Dependence in the Presence of Covariates (from Spatial Analysis for the Social Sciences by David Darmofal) \nDigital copies of the book are available from the UM Library.
URL:https://micde.umich.edu/event/reading-and-discussion-group-spatial-analysis-in-social-sciences-2/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220330T100000
DTEND;TZID=America/Detroit:20220330T120000
DTSTAMP:20230217T195811Z
CREATED:20220204T174533Z
LAST-MODIFIED:20230217T195811Z
UID:10000556-1648634400-1648641600@micde.umich.edu
SUMMARY:Software Development For Research: Git for Collaborative Development
DESCRIPTION:This is a continuation of the previous workshop “Software Development For Research: Version Control Principles”. \nNow\, having learned the basics of version control\, we will see how to use the distributed features of Git to publish your project\, interact with your collaborators\, and incorporate changes from volunteer contributors; for this\, we will utilize GitHub\, the well-known software collaboration platform and code repository. After completing the workshop you will have a good understanding of typical GitHub workflow\, will know how to share your work and collaborate on GitHub. \nTo get the most from this course it is recommended to have a basic understanding of Git concepts (commits\, branches\, merges)\, to the extent covered in the previous workshop. \nThis is part of a series of workshops focused on both technical and soft skills regarding software engineering from a research perspective. \nPlease register at least 48 hours in advance.
URL:https://micde.umich.edu/event/software-development-for-research-git-for-collaborative-development/
LOCATION:Your Desktop
CATEGORIES:Workshops
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