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9 days ago
Unfiled. Edited by Karl Gustafson 9 days ago
March 31: Presenters: Alex Feltus, Claris Castillo/SciDAS: National CyberInfrastructure for Scientific Data Analysis at Scale
Karl G April 28: Presenter: Vas Vasiliadis/Globus: Simplifying Research Data Management via SaaS
April 28: Presenter: Vani Mandava/Microsoft Azure
Lea S Meeting 11March 31, 2017 3:00-4:30 PM ET
Join WebEx here | Meeting number: 310 282 152 | Password: mcYte242
Audio connection: +1-415-655-0003 US TOLL 
Karl G Demo & Discussion  
  • Florence Hudson & John Moore: Internet2 Community Support for the Regional BD Hubs | The community provides information and entertaining discussion on infrastructure, researcher engagement, the data sharing spoke workshop, and how the Internet2 member-based collaborative innovation community is participating in      distributed big data and analytics opportunities and innovations.
  • Florence Hudson is Senior Vice President and Chief Innovation Officer at Internet2, a not for profit consortium of 315+ academic institutions and 150+ research organizations in industry, networking and government. 
  • John Moore is currently the Associate Vice President of Network Architecture and Planning for  Internet2, where he leads a talented group of engineers responsible for developing the  next generation infrastructure that supports the Internet2 community’s quest for  discovery and innovation
  • Claris Castillo & Alex Feltus: SciDAS | National CyberInfrastructure for Scientific Data Analysis at Scale. SciDAS is designed to improve flexibility and accessibility to national resources, helping researchers more effectively use a broader array of these resources. SciDAS is developed using large-scale systems biology and hydrology datasets, but is extensible to many other domains.
  • Alex Feltus is an Associate Professor in Clemson University’s Dept. of Genetics & Biochemistry and CEO of Allele Systems LLC.
  • Claris Castillo is a Senior Computational and Networked Systems Researcher in the Renaissance Computing Institute (RENCI) at UNC Chapel Hill.
Participants () [ Please add your name and email]: 
  • Internet2 Community Support for the Regional BD Hubs
  • SciDAS
  • Thank you all. If you want to provide a demo to the group, please contact Lea Shanley lshanley@renci.org and Karl Gustafson.
38 days ago
Unfiled. Edited by Renata Rawlings-Goss 38 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
41 days ago
Unfiled. Edited by Renata Rawlings-Goss 41 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
47 days ago
Unfiled. Edited by arjun sawhney 47 days ago
47 days ago
Unfiled. Edited by Renata Rawlings-Goss 47 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
59 days ago
Unfiled. Edited by Karl Gustafson 59 days ago
Participants (15) (please add your email address)
Participants (5):
129 days ago
Unfiled. Edited by Renata Rawlings-Goss 129 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. 
Video Now Available for Each Session! Click Link Below:
Members (129)
arjun sawhney jason coposky Prof Dr David Worrall Steve Slota Charity Hilton Giti Javidi Jinfeng Zhang Ge Zhang Matthew DeAngelis Andrew S. Hoffman Yi Jiang Grant West Claire Hardgrove Dylan Young Jen Metes Erin Mullenix Cynthia Lee Zsolt Kira James Myers Rebecca Koskela

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