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X-WR-CALNAME:Institute for Digital Research and Education
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X-WR-CALDESC:Events for Institute for Digital Research and Education
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200813T100000
DTEND;TZID=America/Los_Angeles:20200813T120000
DTSTAMP:20260406T094625
CREATED:20200623T000641Z
LAST-MODIFIED:20200917T061853Z
UID:17328-1597312800-1597320000@idre.ucla.edu
SUMMARY:Essential Command Line Tools in HPC Environments
DESCRIPTION:On macOS\, Linux\, or Windows with Cygwin\, or other POSIX-compliant environments\,​ a number of versatile\, time-tested command-line open-source tools with text-based user interface (TUI) are available to support performing common tasks that arise in advanced computation or data analysis. Unlike their graphical user interface (GUI) counterparts\, which appear to be prevalent in today’s computing environments\, these command-line tools can achieve (almost) infinite flexibility\, efficiency\, and reproducibility if used properly. For example\, when connecting with a remote server (e.g. a computing cluster)\, using the text-based tool is lightweight and fast without the overhead of launching the X11/graphical server on the local computer. Using text-based tools is not as hard (or mysterious) as it may sound once the basics are understood. Once using TUI becomes muscle memory\, one can focus on the subject of interest/research (e.g. editing a script/program or writing a manuscript) instead of operating the complex GUI. In this class\, we will explain and demonstrate a number of such text-based command-line tools by examples and use cases. We will also discuss useful tips for configuring (customizing) these tools. All of these tools can be easily installed on a personal computer\, a remote (or “cloud”) server\, or one’s supercomputer account without superuser privilege. Their use is identical across the platforms\, and they are expected to exist for many years to come. Specifically\, we will explore the following topics: \n\nvim text editors: demo of useful features\nssh (secure shell): beyond a login tool\ngit (version control) + vim integration examples\ntmux (terminal multiplexer): robust persistent console\nshell and shell script basics: important tips\n\nDue to time constraints\, each topic is not expected to be an exhaustive discussion of the underlying tools. \nPrerequisite: The attendee is expected to have a Hoffman2 cluster account in order to be able to follow along with the demos. See this page for account application information. \nRegister in advance:  Click here to REGISTER using the Zoom Meeting Link  \nIf you have any further questions regarding the workshop\, please contact instructor Shao-Ching Huang.
URL:https://idre.ucla.edu/calendar-event/essential-command-line-tools-in-the-hpc-environment
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200812T120000
DTEND;TZID=America/Los_Angeles:20200812T140000
DTSTAMP:20260406T094625
CREATED:20200623T000352Z
LAST-MODIFIED:20200623T000352Z
UID:17325-1597233600-1597240800@idre.ucla.edu
SUMMARY:Running Applications on the Hoffman2 Cluster - Intro
DESCRIPTION:Description coming soon. \n  \nIf you have any further questions regarding the workshop\, please contact instructor Raffaella D’Auria.
URL:https://idre.ucla.edu/calendar-event/running-applications-on-the-hoffman2-cluster-intro
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200811T140000
DTEND;TZID=America/Los_Angeles:20200811T160000
DTSTAMP:20260406T094625
CREATED:20200623T000113Z
LAST-MODIFIED:20200721T184300Z
UID:17322-1597154400-1597161600@idre.ucla.edu
SUMMARY:Introduction to Three Dimensional Graphing and WebVR
DESCRIPTION:We are increasingly sharing our data and research in virtual environments. WebVR (or WebXR as it’s now known) is an open standard that allows 3D / virtual reality (VR) experiences to be produced through your browser. It also aims to make your immersive data platform-agnostic\, so it will be portable to whatever device you have. In this workshop\, you will be introduced to ways to graph three dimensional data. You will also be shown how to bring that 3D graph into a browser environment so that your work can be shared through the Web.In the hands-on segment of the workshop\, we will be using the following tools:\n\nR\nA-Frame\nJupyter Notebooks\n\nThere are no pre-requisite requirements to take this workshop.\n  \nIf you have any further questions regarding the workshop\, please contact instructor Francesca Albrezzi.
URL:https://idre.ucla.edu/calendar-event/introduction-to-three-dimensional-graphing-and-webvr
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200807T110000
DTEND;TZID=America/Los_Angeles:20200807T120000
DTSTAMP:20260406T094625
CREATED:20200717T204426Z
LAST-MODIFIED:20200717T205701Z
UID:18324-1596798000-1596801600@idre.ucla.edu
SUMMARY:National Leadership Class Computing Resources and Opportunities for UCLA Researchers
DESCRIPTION:The Department of Energy\, National Science Foundation\, and the National Aeronautical Space Agency support an ecosystem of “leadership class” computing facilities housing some of the world’s most advanced supercomputers and high-end visualization and data analysis resources. These facilities provide “free” computing cycles at scale and storage to researchers from academia. Access to these resources is obtained through an application process based on the merit of the research objectives and demonstration of the efficacy and parallel scalability of the software.\nThis presentation aims to explain the capabilities of various “leadership class” computing facilities. It will also describe how IDRE may help transition UCLA researchers from local resources and take advantage of these “free” magnificent computing facilities. \nThe presentation will be virtual via zoom. The link will be sent once rsvp is received. \nRSVP link: https://ucla.zoom.us/meeting/register/tJElduGuqDIqGtVUsyivGzy_lPMqT0dzM-ZE
URL:https://idre.ucla.edu/calendar-event/national-leadership-class-computing-resources-and-opportunities-for-ucla-researchers-4
LOCATION:Zoom
CATEGORIES:Education and Training,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200806T100000
DTEND;TZID=America/Los_Angeles:20200806T120000
DTSTAMP:20260406T094625
CREATED:20200622T235457Z
LAST-MODIFIED:20200724T203121Z
UID:17316-1596708000-1596715200@idre.ucla.edu
SUMMARY:Deep Learning in Medicine
DESCRIPTION:Recent years have witnessed a dramatic resurgence of interest in applying deep learning in various research and application areas. In this 2 hour session\, we will present a broad and high-level overview on what deep-learning technologies can do for the domains of medicine and healthcare. Our discussion will focus on the three major application fields: medical imaging analysis\, natural language processing and deep reinforcement learning. In each field\, some of the landscape will be sketched by taking a brief literature survey on the current deep-learning-related research. At the same time we will dig into a couple of specific topics via reviewing some recent exciting achievements as illustrating examples for the subject. The talk will be closed with a general discussion about the challenges of deep learning in the medical and healthcare field. \nThis workshop is for all audiences\, which means the presentation is oriented for people with a wide range of backgrounds (e.g. from data scientists to medical professionals) and for both novices and experts. \nPlease REGISTER in advance for this Zoom Meeting before joining. \nRegister \nAfter registering\, you will receive a confirmation email containing information about joining the meeting. \nIf you have any further questions regarding the workshop\, please contact instructor Qiyang Hu.
URL:https://idre.ucla.edu/calendar-event/deep-learning-in-medicine
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Conferences and Seminars,Education and Training,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200804T140000
DTEND;TZID=America/Los_Angeles:20200804T160000
DTSTAMP:20260406T094625
CREATED:20200622T235253Z
LAST-MODIFIED:20200715T223025Z
UID:17313-1596549600-1596556800@idre.ucla.edu
SUMMARY:Introduction to GIS for the Social Sciences
DESCRIPTION:QGIS workshop for processing and visualizing demographic census data\, commonly used in the social sciences. \n  \nIf you have any further questions regarding the workshop\, please contact instructor Albert Kochaphum.
URL:https://idre.ucla.edu/calendar-event/introduction-to-gis-for-the-social-sciences
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200803T130000
DTEND;TZID=America/Los_Angeles:20200803T160000
DTSTAMP:20260406T094625
CREATED:20200622T234954Z
LAST-MODIFIED:20200706T202253Z
UID:17310-1596459600-1596470400@idre.ucla.edu
SUMMARY:Multiple Imputation in Stata
DESCRIPTION:The purpose of this workshop is to discuss commonly used techniques for handling missing data and common issues that could arise when these techniques are used. In particular\, we will focus on the one of the most popular methods\, multiple imputation and how to perform it in Stata. The Stata code for this seminar is developed using Stata 15. \nAttendance is restricted to researchers from the University of California.  The information for the Zoom meeting will be sent the day before the workshop. \nThe notes for the workshop are here. \nIf you have any further questions regarding the workshop\, please contact instructor Siavash Jalal.
URL:https://idre.ucla.edu/calendar-event/multiple-imputation-in-stata-2
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200728T090000
DTEND;TZID=America/Los_Angeles:20200728T120000
DTSTAMP:20260406T094625
CREATED:20200622T234612Z
LAST-MODIFIED:20200623T184803Z
UID:17305-1595926800-1595937600@idre.ucla.edu
SUMMARY:Introduction to SQL
DESCRIPTION:SQL is a standard language designed to query and extract data from tables stored in a database.  In this short class\, you will learn how to store\, query\, and manipulate data with SQL\, and you will gain exposure to the fundamentals of SQL and relational database management systems. \nRegister in advance:  Please REGISTER using the Zoom Meeting Link before joining! \nIf you have questions regarding the workshop\, please contact Ben Winjum. \n 
URL:https://idre.ucla.edu/calendar-event/introduction-to-sql
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200721T090000
DTEND;TZID=America/Los_Angeles:20200721T110000
DTSTAMP:20260406T094625
CREATED:20200622T234028Z
LAST-MODIFIED:20200706T163413Z
UID:17302-1595322000-1595329200@idre.ucla.edu
SUMMARY:Introduction to GIS/Spatial Research with Python
DESCRIPTION:Register in advance: Please REGISTER using the Zoom Meeting Link before joining! \n\n  \nThere are no pre-requisites to take this workshops\, but it is recommended that you take the following two workshops: \n\nIntroduction to Jupyter\nVersion control with git\n\nAs members of an academic community with a myriad of research initiatives\, the need to map information\, to spatially analyze\, to geoprocess\, and to visualize space and time is becoming ubiquitous. But how do you get started? The answer to this question largely depends on what you want to map\, and how you want to create your map. The what is usually easy. It is driven by your passion to pursue a research question and to have maps assist in the development of an argument. The how is the difficult part. It may depend on what data you have\, what tools you have access to\, how comfortable you are with software packages\, if you are open to write code or use the command line. Often times\, your research question lends itself to a spatial argument. However\, providing insights to questions using spatial visualization tools is a process that involves any number of factors\, including: data acquisition\, data cleanup\, geo-enabling data\, geocoding data\, georeferencing data\, visualizing spatial data\, overlaying other spatial data\, conducting spatial analysis and/or geoprocessing\, analyzing results\, visualizing results\, and publishing results. \nIn the hands-on segment of the workshop\, we will be using the following tools to learn how to analyze and map COVID-19 data: \n\nGitHub\nPython\nJupyter Notebooks\n\nThere are no pre-requisite requirements to take this workshop. \nRegister in advance: Please REGISTER using the Zoom Meeting Link before joining! \nIf you have any further questions regarding the workshop\, please contact the instructor Yoh Kawano.
URL:https://idre.ucla.edu/calendar-event/introduction-to-gis-spatial-research-with-python
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200714T090000
DTEND;TZID=America/Los_Angeles:20200714T120000
DTSTAMP:20260406T094625
CREATED:20200622T233729Z
LAST-MODIFIED:20200623T184825Z
UID:17299-1594717200-1594728000@idre.ucla.edu
SUMMARY:Version Control with Git
DESCRIPTION:Git is a software tool that helps users manage changes to their software over time. Git will allow you to maintain a complete change history of every file\, create branches for concurrent streams of changes\, trace changes with annotations\, and collaborate and share work with others. This interactive introduction will demonstrate how to use Git to track changes\, to explore history\, and to use web-based Git repositories to share work with others and collaborate. \nRegister in advance:  Please REGISTER using the Zoom Meeting Link before joining! \nIf you have questions regarding the workshop\, please contact Ben Winjum.
URL:https://idre.ucla.edu/calendar-event/version-control-with-git-3
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200707T090000
DTEND;TZID=America/Los_Angeles:20200707T110000
DTSTAMP:20260406T094625
CREATED:20200622T233527Z
LAST-MODIFIED:20200623T184846Z
UID:17296-1594112400-1594119600@idre.ucla.edu
SUMMARY:Introduction to Jupyter
DESCRIPTION:The Jupyter Notebook is a computing tool that allows users to edit and run Python\, R\, Julia (and many other programming languages) inside a web browser. Furthermore\, it is a powerful tool that allows users to combine live code\, text\, and visualizations in an interactive\, shareable\, reproducible document. It is a popular tool in many scientific and research disciplines as a data exploration and analysis tool that encourages collaboration and reproducible science. This class will provide an interactive demonstration showing what the Jupyter Notebook is\, how to use it\, and how it is being used in many different fields. \nRegister in advance:  Please REGISTER using the Zoom Meeting Link before joining! \nIf you have questions regarding the workshop\, please contact Ben Winjum.
URL:https://idre.ucla.edu/calendar-event/introduction-to-jupyter-5
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200605T120000
DTEND;TZID=America/Los_Angeles:20200605T130000
DTSTAMP:20260406T094625
CREATED:20200520T201957Z
LAST-MODIFIED:20200605T230136Z
UID:17216-1591358400-1591362000@idre.ucla.edu
SUMMARY:Epidemic Model Guided Machine Learning for COVID-19 Forecasts
DESCRIPTION:Time:  12:00 PM – 1:00 PM\nDate: June 05\, 2020\nLocation:  Zoom (you will receive an email after your rsvp) \nRSVP HERE \nLink to the presentation’s recording and slides \n\n\nAbstract: The novel coronavirus (COVID-19)\, which causes an acute respiratory disease in humans\, has emerged as a global pandemic\, and caused an over 250\,000 death toll in the world. Our lab recently launched a project (https://covid19.uclaml.org) to use machine learning to better understand the spread of COVID-19 and further facilitate the decision making of the government agencies. In this talk\, I will focus on an epidemic model-guided machine learning approach for the confirmed case and death forecasts for COVID-19\, and peak date projection in both state and national level. In specific\, we found that standard epidemic models such as SIR and SEIR are insufficient for modeling the spread of COVID-19. We therefore propose a variant of the SEIR model that takes into account the untested/unreported cases of COVID-19\, and then use a machine learning algorithm to train this model. Validation based on a week ahead prediction indicates that our model is more accurate than many other models including the model proposed by IHME at the University of Washington. \nAbout Speaker: Dr. Quanquan Gu is an Assistant Professor of Computer Science at UCLA. His current research is in the area of artificial intelligence and machine learning\, with a focus on developing and analyzing nonconvex optimization algorithms for machine learning and building the theoretical foundations of deep learning. He received his Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign in 2014. He is a recipient of the Yahoo! Academic Career Enhancement Award\, NSF CAREER Award\, Simons Berkeley Research Fellowship\, Adobe Data Science Research Award\, Salesforce Deep Learning Research Award and AWS Machine Learning Research Award.
URL:https://idre.ucla.edu/calendar-event/covid-19-forecasts
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Conferences and Seminars,Presentations,UCLA event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200527T100000
DTEND;TZID=America/Los_Angeles:20200527T120000
DTSTAMP:20260406T094625
CREATED:20200415T214258Z
LAST-MODIFIED:20200422T023722Z
UID:16511-1590573600-1590580800@idre.ucla.edu
SUMMARY:Running Applications on the Hoffman2 Cluster: Part II
DESCRIPTION:This class will address the process of creating MATLab standalone executables and running MATLab in batch\, as well as running Abaqus python scripts. It will give examples of how to submit array jobs using Abaqus\, R and other applications. Can my simulations be submitted as array jobs? Bring your own problem. Also\, Jupyter Notebooks: another way to run python and R on Hoffman2. \nInformation for the Zoom meeting will be emailed the day before the workshop.  If you have questions regarding the workshop\, please contact the instructor Raffaella D’Auria.
URL:https://idre.ucla.edu/calendar-event/running-applications-on-the-hoffman2-cluster-part-ii-2
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200526T090000
DTEND;TZID=America/Los_Angeles:20200526T120000
DTSTAMP:20260406T094625
CREATED:20200417T175632Z
LAST-MODIFIED:20200422T023530Z
UID:16555-1590483600-1590494400@idre.ucla.edu
SUMMARY:Talk: "Power and Sample Size" with Stata Corp's Chuck Huber
DESCRIPTION:In this talk\, I introduce the concepts and jargon of power and sample size calculations such as alpha levels\, power\, and minimum detectable effect sizes.  I do several simple calculations manually and then demonstrate how to replicate these calculations using Stata’s -power- commands.  Next\, I demonstrate how to create tables and graphs for power\, sample size\, and minimum detectable effect sizes for a range of values. We will conclude with a discussion of strategies to increase statistical power. \nThe second part of the talk demonstrates how to calculate power using simulation methods and how to create your own custom power calculation programs that leverage Stata’s -power- command to create custom tables and graphs.  We will work examples that simulate power for a t-test\, the interaction term in a linear regression model\, and the interaction term in a multilevel model.  Along the way you will learn how to create simulated datasets\, use Stata’s -simulate- command\, and how to write your own Stata commands using -program- and -syntax-. \nThe information for the Zoom meeting will be sent out the day before the presentation.
URL:https://idre.ucla.edu/calendar-event/talk-power-and-sample-size-with-stata-corps-chuck-huber
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training,Presentations
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200522T100000
DTEND;TZID=America/Los_Angeles:20200522T120000
DTSTAMP:20260406T094625
CREATED:20200413T172412Z
LAST-MODIFIED:20200513T183225Z
UID:16403-1590141600-1590148800@idre.ucla.edu
SUMMARY:Using SQL with Python for Data Analysis
DESCRIPTION:SQL is a standard language designed to query and extract data from tables stored in a database.  Python\, on the other hand\, has well-known libraries specially designed for data analysis and manipulation.  This course will introduce attendees to the basics of SQL\, relational database management systems\, and options for integrating these with Python for data analysis.  No prior experience with SQL will be assumed;  prior experience with Python will be useful. \nPlease REGISTER using Zoom Meeting Link before joining! \nIf you have questions regarding the workshop\, please contact the instructor Ben Winjum.
URL:https://idre.ucla.edu/calendar-event/using-sql-with-python-for-data-analysis
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200520T100000
DTEND;TZID=America/Los_Angeles:20200520T120000
DTSTAMP:20260406T094625
CREATED:20200415T214214Z
LAST-MODIFIED:20200415T214214Z
UID:16508-1589968800-1589976000@idre.ucla.edu
SUMMARY:Running Applications on the Hoffman2 Cluster: Part I
DESCRIPTION:The Hoffman2 cluster is a powerful computational resource for the UCLA research community. This class is designed to clarify the process of getting started on the cluster\, navigating the command line and porting your own applications or using applications already available on the cluster. It also addresses how to import your workflow to Hoffman2 and how to submit batch and run interactive applications. \nInformation for the Zoom meeting will be emailed the day before the workshop.  If you have questions regarding the workshop\, please contact the instructor Raffaella D’Auria.
URL:https://idre.ucla.edu/calendar-event/running-applications-on-the-hoffman2-cluster-part-i-2
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200518T130000
DTEND;TZID=America/Los_Angeles:20200518T160000
DTSTAMP:20260406T094625
CREATED:20200310T174254Z
LAST-MODIFIED:20200414T233118Z
UID:15987-1589806800-1589817600@idre.ucla.edu
SUMMARY:A Practical Introduction to Factor Analysis in SPSS
DESCRIPTION:This workshop will give a practical overview of exploratory (EFA) in SPSS. Topics to be covered include factor extraction\, principal components analysis\, estimation methods\, factor rotation\, refining the factor structure\, and generating factor scores for subsequent analyses. \nThe notes for the workshop are here.  This workshop will not be hands-on. \nAttendance is restricted to researchers from the University of California.  The information for the Zoom meeting will be sent the day before the workshop.
URL:https://idre.ucla.edu/calendar-event/a-practical-introduction-to-factor-analysis-in-spss
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200515T100000
DTEND;TZID=America/Los_Angeles:20200515T120000
DTSTAMP:20260406T094625
CREATED:20200413T203919Z
LAST-MODIFIED:20200513T182801Z
UID:16400-1589536800-1589544000@idre.ucla.edu
SUMMARY:Exploring Data and Machine Learning with Interactive Python Tools
DESCRIPTION:This course will teach attendees how to build interactive widgets and visualizations for exploring equations\, datasets\, and machine learning models. This course will touch on several machine learning tools in Python\, but the primary goal will be to give attendees a foundation in tools that can be useful in exploring datasets and conceptualizing models. Basic familiarity with Python will be useful. \nPlease REGISTER using Zoom Meeting Link before joining! \nIf you have questions regarding the workshop\, please contact the instructor Ben Winjum.
URL:https://idre.ucla.edu/calendar-event/exploring-data-and-machine-learning-with-interactive-python-tools
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200513T140000
DTEND;TZID=America/Los_Angeles:20200513T160000
DTSTAMP:20260406T094625
CREATED:20200401T174253Z
LAST-MODIFIED:20200423T193947Z
UID:16053-1589378400-1589385600@idre.ucla.edu
SUMMARY:Rebel Story Mapping: Critical Perspectives on Data Visualizations
DESCRIPTION:Learn how to break the rules of mapmaking! This workshop’s main lecture introduces disruptive mapping practices from critical cartography and indigenous critical theory and applies them to a Critical COVID-19 Story map built on Esri StoryMaps platform. The technical portion focuses on learning how to set up an Esri Public Account\, incorporating media and external digital content\, and highlighting the various building blocks within Esri StoryMaps. Participants are encouraged to walk away contemplating their role as a data analyst and question power relationships with maps. \n\nAttendance is restricted to researchers from the University of California.  The information for the Zoom meeting will be sent the day before the workshop.
URL:https://idre.ucla.edu/calendar-event/rebel-story-mapping-critical-perspectives-on-data-visualizations
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200512T100000
DTEND;TZID=America/Los_Angeles:20200512T120000
DTSTAMP:20260406T094625
CREATED:20200401T211706Z
LAST-MODIFIED:20200508T155125Z
UID:16077-1589277600-1589284800@idre.ucla.edu
SUMMARY:Data Processing and Visualization using MATLAB
DESCRIPTION:MATLAB\, developed by MathWorks\, has been used in research and the industry for computations\, simulations\, data analysis and visualization across multiple disciplines such as physical sciences\, engineering\, economics\, and biomedical fields. This class will discuss and demonstrate how to import data\, perform data analysis\, trend analysis\, and various data/file exchange formats using the MATLAB desktop command and graphical interface. It will also demonstrate visualization techniques to present the results in appropriate forms\, such as 2D or 3D graphs and animation. \nThis class is particularly useful for those who already have some basic concepts of MATLAB and want to advance their knowledge and skills in this language for data processing and visualization in practice. It is highly recommended to have MATLAB installed on your laptop computer in advance and bring it to the class. For installation details\, please see https://softwarecentral.ucla.edu/matlab-getmatlab  \nThe code and data files used in the class are available at: https://github.com/jhyhuang/idre_matlab_dp_vis/ \nPlease REGISTER using Zoom Meeting Link before joining!
URL:https://idre.ucla.edu/calendar-event/data-processing-and-visualization-using-matlab
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200511T130000
DTEND;TZID=America/Los_Angeles:20200511T160000
DTSTAMP:20260406T094625
CREATED:20200310T173732Z
LAST-MODIFIED:20200414T233256Z
UID:15984-1589202000-1589212800@idre.ucla.edu
SUMMARY:R Data Management
DESCRIPTION:This workshop introduces R packages and functions that help users import\, transform and manage their data in preparation for analysis.   The seminar focuses on the “tidy data” philosophy encouraged y the “tidyverse” collection of R packages\, but draws on other packages for useful functions as needed.  Topics include data import\, transforming variables\, string functions\, missing data\, merging datasets\, reshaping data\, grouped data processing\, and looping. \nThe notes for the workshop are here.  This workshop will not be hands-on. \nAttendance is restricted to researchers from the University of California.  The information for the Zoom meeting will be sent the day before the workshop.
URL:https://idre.ucla.edu/calendar-event/r-data-management
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200507T100000
DTEND;TZID=America/Los_Angeles:20200507T113000
DTSTAMP:20260406T094625
CREATED:20200401T211457Z
LAST-MODIFIED:20200409T205355Z
UID:16073-1588845600-1588851000@idre.ucla.edu
SUMMARY:Learning Deep Learning with PyTorch (6) Recurrent Neural Networks and LSTM
DESCRIPTION:This workshop series is to present overviews to the exciting deep learning techniques and to provide a practical guide for general audience to step into the field. It will be primarily appropriate for the beginners who want to learn the techniques and apply to their future research activities. Researchers with deep learning experiences are expected to get benefits from related discussions as well. \nIn the sixth session of the series\, we will introduce recurrent neural networks (RNNs) and illustrate how to apply RNNs and LSTM models to solve a simple sequence problem with PyTorch. The knowledge of topics covered in the previous sessions is assumed. Working experience of Python\, Jupyter Notebooks and linear algebra will be helpful. \nIf you have any further questions regarding the workshop\, please contact instructor Qiyang Hu. \nPlease REGISTER to the Zoom Meeting before joining.
URL:https://idre.ucla.edu/calendar-event/learning-deep-learning-with-pytorch-6-recurrent-neural-network-and-lstm
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200504T130000
DTEND;TZID=America/Los_Angeles:20200504T160000
DTSTAMP:20260406T094625
CREATED:20200310T172947Z
LAST-MODIFIED:20200414T233404Z
UID:15981-1588597200-1588608000@idre.ucla.edu
SUMMARY:Introduction to Mplus
DESCRIPTION:Mplus is a powerful statistical package used for the analysis of latent variables. Among the kinds of analysis it can perform are exploratory factor analysis\, confirmatory factor analysis\, latent class analysis\, latent growth curve modeling\, structural equation modeling and multilevel modeling. The program can handle a combination of categorical and continuous variables and often permits missing data. It integrates these analyses into a single framework where you can combine techniques like growth curve modeling and latent class analysis to ask unique questions\, such as “Are there latent classes among the growth trajectories?”. Mplus runs under Windows. This workshop is designed for people who are just getting started using Mplus to orient them to the nuts and bolts of using this package. These notes are the scripts for the workshop. The notes are not meant to be a Mplus textbook or substitute for the reference manual. \nThe notes for the workshop are here.  This workshop will not be hands-on. \nAttendance is restricted to researchers from the University of California.  The information for the Zoom meeting will be sent the day before the workshop. \n 
URL:https://idre.ucla.edu/calendar-event/introduction-to-mplus-2
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200504T100000
DTEND;TZID=America/Los_Angeles:20200504T113000
DTSTAMP:20260406T094625
CREATED:20200401T211355Z
LAST-MODIFIED:20200409T205305Z
UID:16070-1588586400-1588591800@idre.ucla.edu
SUMMARY:Learning Deep Learning with PyTorch (5) Data Augmentation and Transfer Learning
DESCRIPTION:This workshop series is to present overviews to the exciting deep learning techniques and to provide a practical guide for general audience to step into the field. It will be primarily appropriate for the beginners who want to learn the techniques and apply to their future research activities. Researchers with deep learning experiences are expected to get benefits from related discussions as well. \nIn the fifth session of the series\, we will introduce data augmentation and transfer learning techniques to get a better solution for deep learning projects with PyTorch. The knowledge of topics covered in the previous sessions is assumed. Working experience of Python\, Jupyter Notebooks and linear algebra will be helpful. \nIf you have any further questions regarding the workshop\, please contact instructor Qiyang Hu. \nPlease REGISTER to the Zoom Meeting before joining.
URL:https://idre.ucla.edu/calendar-event/learning-deep-learning-with-pytorch-5-data-augmentation-and-transfer-learning
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200429T100000
DTEND;TZID=America/Los_Angeles:20200429T113000
DTSTAMP:20260406T094625
CREATED:20200401T211248Z
LAST-MODIFIED:20200427T203703Z
UID:16067-1588154400-1588159800@idre.ucla.edu
SUMMARY:Learning Deep Learning with PyTorch (4) Convolutional Neural Networks
DESCRIPTION:This workshop series is to present overviews to the exciting deep learning techniques and to provide a practical guide for general audience to step into the field. It will be primarily appropriate for the beginners who want to learn the techniques and apply to their future research activities. Researchers with deep learning experiences are expected to get benefits from related discussions as well. \nIn the fourth session of the series\, we will introduce convolutional neural network and learn how to use PyTorch to do image processing for classic Dogs-vs-Cats problem. The knowledge of topics covered in the previous sessions is assumed. Working experience of Python\, Jupyter Notebooks and linear algebra will be helpful. \nIf you have any further questions regarding the workshop\, please contact instructor Qiyang Hu. \nPlease REGISTER to the Zoom Meeting before joining.
URL:https://idre.ucla.edu/calendar-event/learning-deep-learning-with-pytorch-4-convolutional-neural-networks-2
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200429T090000
DTEND;TZID=America/Los_Angeles:20200429T120000
DTSTAMP:20260406T094625
CREATED:20200401T173944Z
LAST-MODIFIED:20200424T194041Z
UID:16046-1588150800-1588161600@idre.ucla.edu
SUMMARY:Intro to GIS: Getting Started with Spatial Research
DESCRIPTION:As members of an academic community with a myriad of research initiatives\, the need to map information\, to spatially analyze\, to geoprocess\, and to visualize space and time is becoming ubiquitous. But how do you get started? The answer to this question largely depends on what you want to map\, and how you want to create your map. The what is usually easy. It is driven by your passion to pursue a research question and to have maps assist in the development of an argument. The how is the difficult part. It may depend on what data you have\, what tools you have access to\, how comfortable you are with software packages\, if you are open to write code or use the command line. Often times\, your research question lends itself to a spatial argument. However\, providing insights to questions using spatial visualization tools is a process that involves any number of factors\, including: data acquisition\, data cleanup\, geo-enabling data\, geocoding data\, georeferencing data\, visualizing spatial data\, overlaying other spatial data\, conducting spatial analysis and/or geoprocessing\, analyzing results\, visualizing results\, and publishing results. There is no single application that can accomplish every phase of your spatial process.  \nIn the hands-on segment of the workshop\, we will be using the following tools to learn how to work with COVID-19 data: \n\nGitHub\nPython\nJupyter Notebooks\nTableau\n\nThere are no pre-requisite requirements to take this workshop\, but please make sure to RSVP! Space is limited. \nAttendance is restricted to researchers from the University of California.  The information for the Zoom meeting will be sent the day before the workshop.\nIf you have questions regarding the workshop\, please contact the instructor Yoh Kawano.
URL:https://idre.ucla.edu/calendar-event/intro-to-gis-getting-started-with-spatial-research
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200427T130000
DTEND;TZID=America/Los_Angeles:20200427T160000
DTSTAMP:20260406T094625
CREATED:20200310T172259Z
LAST-MODIFIED:20200414T233505Z
UID:15978-1587992400-1588003200@idre.ucla.edu
SUMMARY:Introduction to ggplot2
DESCRIPTION:The ggplot2 package is a widely-used and well-supported system for creating eye-catching graphics in R.  In this interactive workshop\, you will learn the underlying grammar of graphics that forms the philosophical framework of ggplot2\, giving you the power to create publication-quality figures intuitively.  The workshop is interactive\, in which attendees are encouraged to participate in R coding to create their own statistical graphics. \nThe notes for the workshop are here.  This workshop will be hands-on.  Attendees should have R and R Studio installed on their computers prior to the start of the workshop. \nAttendance is restricted to researchers from the University of California.  The information for the Zoom meeting will be sent the day before the workshop.
URL:https://idre.ucla.edu/calendar-event/introduction-to-ggplot2-3
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200423T100000
DTEND;TZID=America/Los_Angeles:20200423T113000
DTSTAMP:20260406T094625
CREATED:20200401T211157Z
LAST-MODIFIED:20200409T205137Z
UID:16062-1587636000-1587641400@idre.ucla.edu
SUMMARY:Learning Deep Learning with PyTorch (3) Knowing PyTorch
DESCRIPTION:This workshop series is to present overviews to the exciting deep learning techniques and to provide a practical guide for general audience to step into the field. It will be primarily appropriate for the beginners who want to learn the techniques and apply to their future research activities. Researchers with deep learning experiences are expected to get benefits from related discussions as well. \nIn the third session of the series\, we will introduce PyTorch deep learning framework\, illustrate the basic usage of tensors and automatic differentiation\, and solve a simple temerature-conversion problem using PyTorch. The knowledge of topics covered in the first and second session about machine/deep learning is assumed. Working experience of Python\, Jupyter Notebooks will be helpful to follow the demos. \nIf you have any further questions regarding the workshop\, please contact instructor Qiyang Hu. \nPlease REGISTER to the Zoom Meeting before joining.
URL:https://idre.ucla.edu/calendar-event/learning-deep-learning-with-pytorch-3-knowing-pytorch
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200422T120000
DTEND;TZID=America/Los_Angeles:20200422T130000
DTSTAMP:20260406T094625
CREATED:20200221T000214Z
LAST-MODIFIED:20200422T075448Z
UID:15818-1587556800-1587560400@idre.ucla.edu
SUMMARY:IDRE-Early Career Researchers Group Meeting
DESCRIPTION:Time:  12:00 PM – 1:00 PM\nDate: April 22\, 2020\nLocation:  Zoom (the meeting link will be sent to you once you rsvp below) \nRSVP HERE \nIDRE is happy to reschedule the first lunch meeting for the IDRE Early Career Research Group to April 22\, 2020. Although this first meeting is going to be virtual\, through zoom\, our goal remains the same\, i.e.\, to establish a series of meetings\, where you will have an opportunity to share ideas\, ask questions\, find opportunities for collaboration\, and socialize with your peers. \nAt this first meeting\, we will have a 30-minute presentation on “Knowledge Graphs\, Natural Language Processing\, and Standards for Unifying Unstructured Biomedical Data” by J. Harry Caufield. \nThe agenda of the meeting is as follows: \n\n12:00 PM – 12:10 PM: Welcome and Introduction\n12:10 PM – 12:40 PM: Presentation* – Knowledge Graphs\, Natural Language Processing\, and Standards for Unifying Unstructured Biomedical Data by J. Harry Caufield\n12:40 PM – 1:00 PM Q&A\n\n* Presentation: Knowledge Graphs\, Natural Language Processing\, and Standards for Unifying Unstructured Biomedical Data \nSpeaker:\nJ. Harry Caufield\, Ph.D.\,\nIDRE Scholar\,\nUCLA Data Science in Cardiovascular Medicine \n\n\n\nAbstract: \nComputational analysis of clinical events is a promising strategy for developing a comprehensive understanding of highly variable disease presentations. The development and validation of new methods appropriate for this general task are increasingly limited by the availability of carefully annotated\, open\, and diverse datasets of biomedical text. I will discuss our group’s recent efforts to enforce consistent structures and standards on the data within text documents written in the biomedical language. The standards support consistent data models and structures (i.e.\, knowledge graphs) for unifying heterogeneous observations and relationships as well as machine learning approaches for isolating biologically and clinically-relevant insights. I will also introduce our newly produced text datasets\, each of which is richly annotated and freely available. \nAbout Speaker:\nJ. Harry Caufield is a postdoctoral fellow in the NIH HeartBD2K Center of Excellence at UCLA\, where he works with Prof. Peipei Ping of UCLA’s departments of Physiology\, Medicine\, and Bioinformatics. Before joining UCLA\, Dr. Caufield earned his PhD in Integrative Life Sciences at Virginia Commonwealth University\, where he studied microbial protein interactions and developed intuitive methods for working with large protein interaction networks. He continues to have an active interest in learning about biological relationships hidden within disparate data sources\, particularly those with a direct impact on human health and disease.
URL:https://idre.ucla.edu/calendar-event/ecr-meet-presentation-april2020
LOCATION:Zoom
CATEGORIES:Conferences and Seminars,Meetings,Presentations,UCLA event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200420T100000
DTEND;TZID=America/Los_Angeles:20200420T113000
DTSTAMP:20260406T094625
CREATED:20200401T184842Z
LAST-MODIFIED:20200409T205005Z
UID:16059-1587376800-1587382200@idre.ucla.edu
SUMMARY:Learning Deep Learning with PyTorch (2) Mechanics of Deep Learning
DESCRIPTION:This workshop series is to present overviews to the exciting deep learning techniques and to provide a practical guide for general audience to step into the field. It will be primarily appropriate for the beginners who want to learn the techniques and apply to their future research activities. Researchers with deep learning experiences are expected to get benefits from related discussions as well. \nIn the second session of the series\, we will look into the procedures of working on a general deep learning project\, especially on how to train a deep neural network. The knowledge of topics covered in the first session is assumed. Basic knowledge of calculus and linear algebra will be helpful to understand the details. \nIf you have any further questions regarding the workshop\, please contact instructor Qiyang Hu. \nPlease REGISTER to the Zoom Meeting before joining.
URL:https://idre.ucla.edu/calendar-event/learning-deep-learning-with-pytorch-2-mechanics-of-deep-learning-2
LOCATION:Zoom
CATEGORIES:Classes and Workshops,Education and Training
END:VEVENT
END:VCALENDAR