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Renata Rawlings-Goss

256 days ago
 
Participants [ Please add your name and email]: 29+
Lea Shanley, SBDH co-ED, lshanley@renci.org
Karl Gustafson, SBDH Project Manager, kgustafs@renci.org
Daniel W Daniel Wheeler, NIST, daniel.wheeler@nist.gov
Zeydy O Zeydy Ortiz, DataCrunch Lab
Lea S Christine Kirkpatrick
Hong Yi
Jim Myers
Leslie Hsu
Nirav Merchant
Rene Baston
Lea S Illya Baldwin
Howard Lander
Arcot "Raja" Rajasekar
Jim Meyers
S. Srinivasan 
Hong Yi
Len Fishman
Lisa Stillwell
Zhen Hu
Katie
Bonnie Hurst
Renata R Renata Rawlings-Goss, SBDH co-ED, rrawlings.goss@gatech.edu 
 
 
Notes [PLEASE ADD YOUR NOTES AND COMMENTS HERE]:
 
Lea S
  • Websites
  • XSEDE Website: https:/www.xsede.org
  • XDMoD Interface: https:/xdmod.ccr.ubuffalo.edu
  • XSEDE Services
  • Ticket System based on RT
  • SXEDEnet (private net provided by Internet2 AL2S)
  • User Interface and Online Information website, XUP, wiki (confluence)
  • Single Sign on Hub - login.xsede.org
  • XSEDE Certificate Authorities for issuing certs
  • XSEDE RSA 2-factor for SPs
  • Duo two-factor for end users
  • (almost) 7x25 XSEDE Operation Center (XOC)
  • Resource Description Repository: RDR provides repo for detailed info about an XSEDE resource and this information is used in a variety of places including the XUP
  • XSEDE Iinformation servies for resource discovery and description
  • SPS install information servies software
  • XSEDE info services architecture (resource description, central information services, dynamic resource publishing, user portal/gateways/software or service)
  • XSEDE Federation
  • The XSede Federation is a collection of partner resources called Service Providers (SPs) that add value to the national cyberinfrastructure
  • SPs can be Level 1, Level 2, or Level 3 
Victor H
  • Victor maintains a list and there are 7 Level 1 (NSF funded and allocated) SPs, 8  Level 2 SPs (2 NSF funded MRIs which have some portion allocated and 6 unallocated) and 20 Level 3 SPs (generally made up of non-NSF funded campus computing centers or campus resources/staff)
  • See the complete list in the presentation
  • Any of the HUB teams can contact Victor (victor@utk.edu) if they want to discuss further the use of XSEDE services for Big Data Hub purposes.  There is also the possibility (to be determined) if there is extensions to XSEDE services that are needed for the Hubs.
 
Lea S
  • Jetstream, an XSEDE project 
  • Contact Nirva Merchant at Nirave@email.arizona.edu
  • NSF now funding clouds to do research, such as Bridges (Hybrid) and Jetstream
  • Jetstream is a NSF's first production cloud facility, part of the NSF eXtreme Digital (XD) program; provide on-demand interactie computing and analysis; enable configurable environments and architectures; user-friendly, widely accessible cloud environment; user-selectable libary of preconfigured virtual machines.
  • Working on Intel compilers and ebuggers -- Math Kernel Libraries, Data Analytics
  • Workforce development; HBCUs and Minority Serving Institutions and Tribal colleges
  • Who will use Jetstream? For the researcher needing a handful of cores (1 to 44 /vCPU); software crators and researchers neeing to crate their own customized virtual machines, containers and workflows; sicence gateway creators using Jetstream as a frontend or; STEM educators
  • Two levels of access: Interactive user access via web interface and vnc/ssh; peristent access for Science Gateways.
  • Production: 
 
 
  • RADII: REsource Aware Dataentric collaborative Infrastucture
  • A production level resource
  • Leverages exogenii 
  • Goal: To make data-driven colloabrtions a "turn key" experience for domain researchers anda "commodity" for the science community
  • Approach: To develo new cyberinfrastructure approaches and tools to manage data-centric collaborations built based on natural models 
  • Challenges: 
  • Procuring infrastructure is hard; 
  • infrastructure-ownership vs expertise-owenerhsip
  • lack of dedicated network infrastructure (transfer times can take weeks/months)
  • Data Infrastructure gap: limited infrastructure support to moitor and contorl data access and transfer
  • RADII Core
  • RADII allows users to programmatically create durable collaborative infrastructure to support data-centric collaboraitons through out their entire life-cycle
  • RADII relies on dataflow diagram formalism to describe all aspects of a collaboration.
  • Central to their approach is the orchestration of the provisioning of the intrastructure and the data management plane in an integrated fashion.
  • Foundational Technologies
  • ORCA (control software) / ExcoGENI (testbed)
  • RADII conects the two
  • Prototype integration of RADII with NSF HydroShare to support collaborations around data hydrology models including the provisioning of compute to excute models
  • They are looking for more user cases and domain science CI efforts that lack the tools to support their data related activities and collaboraitons. But projects would need to fit within existing infrastructure or come with their own infrastructure.
  • Can do prototyping but can't run full production commercial sites with ExcoGENII. 
 
  • NIST Data Science for Materials & Advanced Manufacturing
...
62 days ago
Unfiled. Edited by Renata Rawlings-Goss 62 days ago
Smart and Connected Cities Community
Lea S The South Hub community is a place for members of the Hub to share their research and initiatives with the community, and to learn about regional and national initiatives in smart, connected and resilient cities and communities.
 
Renata R Join Us
If you would like to give a presentation to the group about your work in smart and connected communities / cities, or if you would like to volunteer to help us organize this community, please contact info@southbdhub.org and notate in the subject "Smart Cities Working Group".
 
Resources
See or contribute resources about Smart  Cities Projects, please add to our list here: SMART CITY RESOURCES
 
 
 
  • WORKSHOP: INTERNET-OF-THINGS FOR SMART AND CONNECTED CITIES AND CAMPUSES
Challenges in Data Analytics and Decision-making from Distributed Sensors
APRIL 25-26, 2017, ATLANTA, GA 
SMART CITY RESOURCES
Workshop Partiscipants from IoT Industry, Academia, Federal and City Government
 
AGENDA: Day 1 | 8:00am – 4:00pm
THE INTERNET-OF-THINGS FOR SMART AND CONNECTED CITIES AND CAMPUSES Day 1 | 8:00am – 4:00pm
8:00 – 8:25 Breakfast and Registration
8:25 – 8:35 Renata Rawlings-Goss | Co-Executive Director of the South Big Data Innovation Hub
8:35 – 8:45 Welcome: Data Science at Georgia Tech
9:00 – 10:15 Managing Director of Intelligent Transportation Systems
10:15 – 10:30 Break
10:30 – 11:45 ~~~~~11:45 - 1:00 pm Lunch~~~~~
1:00 – 1:45 | Innovation Director,  U.S. Department of Homeland Security - HSARPA, Science and Technology Directorate
1:45 – 2:00 Break
2:00 – 3:00 |University of North Carolina
3:00 – 3:45 What is the future of IoT/sensor data?
3:45 – 4:00 Conclusion
 
 
AGENDA: Day 2 | 8:30am – 12:45pm 
THE INTERNET-OF-THINGS FOR SMART AND CONNECTED CITIES AND CAMPUSES Day 2 | 8:30am – 12:45pm
8:30 – 9:00 Breakfast and Registration
9:00 – 10:00 Keynote: Smart Campuses
10:15 – 11:15 | CEO and Founder Cytilife
11:30 – 12:30 Round Table: Collaboration Opportunities
12:30 – 12:45 ~~~~~ End Conference ~~~~~
 
 
131 days ago
Unfiled. Edited by Renata Rawlings-Goss 131 days ago
Dr. Le Song Embedding Graphical Models with Applications to Recommendation Systems, Knowledge Reasoning and Materials Science  
 
Bio: Polo Chau
 
 
Renata R Bio: Le Song
Le Song is an assistant professor in the Department of Computational Science and Engineering, College of Computing, Georgia Institute of Technology. He received his Ph.D. in Machine Learning from University of Sydney and NICTA in 2008, and then conducted his post-doctoral research in the Department of Machine Learning, Carnegie Mellon University, between 2008 and 2011. Before he joined Georgia Institute of Technology in 2011, he was a research scientist at Google briefly. His principal research direction is machine learning, especially kernel methods and probabilistic graphical models for large scale and complex problems, arising from artificial intelligence, network analysis, computational biology and other interdisciplinary domains. He is the recipient of the Recsys’16 Deep Learning Workshop Best Paper Award, AISTATS'16 Best Student Paper Award, IPDPS'15 Best Paper Award, NSF CAREER Award’14, NIPS’13 Outstanding Paper Award, and ICML’10 Best Paper Award. He has also served as the area chair or senior program committee for many leading machine learning and AI conferences such as ICML, NIPS, AISTATS and AAAI, and the action editor for JMLR.
 
Talk abstract
Structured data, such as sequences, trees, graphs and hypergraphs, are prevalent in a number of real world applications such as social network analysis, recommendation systems and knowledge base reasoning. The availability of large amount of such structured data has posed great challenges for the machine learning community. How to represent such data to capture their similarities or differences? How to learn predictive models from a large amount of such data, and efficiently? How to learn to generate structured data de novo given certain desired properties?
In this talk, I will present a structure embedding framework (Structure2Vec), an effective and scalable approach for representing structured data based on the idea of embedding latent variable models into a feature space, and learning such feature space using discriminative information. Interestingly, Structure2Vec extracts features by performing a sequence of nested nonlinear operations in a way similar to graphical model inference procedures, such as mean field (or convolution over graph) and belief propagation. In large scale applications involving materials design, recommendation system and knowledge reasoning, Structure2Vec consistently produces the-state-of-the-art predictive performance. In some cases, Structure2Vec is able to produces a more accurate model yet being 10,000 times smaller.
 
 
Leading to finding mroe relevant nodes: Apolo (Machine learning + interactive Vis)
___________________________
Le Song Machine Learning in Recommendation Systems, Knowledge Graphs, and Materials Research. Can the same approach be used in all three? 
Yes, using graph vectorization as an alternative to matrix vectorization
 
134 days ago
Unfiled. Edited by Renata Rawlings-Goss 134 days ago
Renata R Materials and Advanced Manufacturing
 
 
Resource List, White Paper, and Video Archive Just Released! Links Below! 
 
  • Resources List for Materials Informatics
This Github repository was created after the Materials and Advanced Manufacturing Workshop, from combined participant input. Courtesy of  Andrew Medford , Mark Jack and Jason Hattrick-Simpers. If you know of resources for materials informatics. Feel free to become a contributor.
 
  • High Impact Applications of Data Science for Materials & Manufacturing
This white paper summarizes expert opinions from industry, academic, and government partners of the South Big Data Hub. It focuses on the impact of addressing data challenges in the design of materials and in the process of advanced manufacturing. 
 
 
Photo: Materials Workshop Speakers  
Location: Georgia Institute of Technology, Klaus Advanced Computing Building, Atlanta, GA
 
140 days ago
Unfiled. Edited by Renata Rawlings-Goss 140 days ago
Health Working group
The Big Data in Health theme community is an open forum to discuss data solutions and issues facing modern Healthcare. This pad text is synchronized as you type, so that everyone viewing this page sees the same text.  Please feel free to add to the discussion.
 
Renata R If you would like to give a presentation to the group about your work  in health analytics, health disparities, or health economy or if your would like to volunteer to help organize this community, please contact Renata Rawlings-Goss at rrawlingsgoss@gatech.edu 
 
  • See Our Funded Spoke in Health 
 
 
  • Meeting: Nov 14, 2:30 - 3:30 pm ET
 
300 days ago
Unfiled. Edited by Karl Gustafson , Renata Rawlings-Goss 300 days ago
Karl G Environment and Natural Hazards Community Call
Sept 16, 11:00-12:15 pm EDT
 
 
 
222 days ago
Unfiled. Edited by Renata Rawlings-Goss 222 days ago
Renata R See Our New Funded Spoke in Energy 
 
  • Applications of Analytics and Machine Learning in Energy Industry-Academia Workshop 
The goal is to connect industry partners with academic researchers in the domains of Energy: Power, Smart Grid, etc as well as Big Data and Data Science. Speakers will be specifically selected to share their perspective on high-impact applications or challenges surrounding the use of data science, analytics, informatics, and machine learning  in the Energy space.  Attendees will come from academic research institutions across the 16 states that comprise the South Big Data Innovation Hub and industrial partners across the country. Participants will engage in active scoping and round-table discussions in order to build partnerships across high-impact application verticals.
 
Georgia Institute of Technology, Klaus Advanced Computing Building, Atlanta, GA
Date: September 6, 2016
Time: 8:00 am to 6:00 pm 
Watch: Live Video
 
 
The South Big Data Innovation Hub accelerates partnerships among people in business, academia, and government that apply data science and analytics to societal and economic challenges important to the region. The South Hub is part of a national network of four Big Data Innovation Hubs in the United States, and individually includes more than 500 members from both the private and public sectors. 
 
Agenda
Video Now Available for Each Session! Click Link Below:
 
 

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