Jennifer Hammock:The Biodiversity Open Data Landscape | The biodiversity sciences community has made rapid strides in mobilizing and sharing data in the last ten years. These data are heterogenous for a number of inherent and historic reasons, so as the data are made available (digitized and disseminated) the accompanying data integration and interpretation work is a significant challenge. Interoperability and evaluating fitness for use is still complicated, but cultural and policy barriers to sharing have largely been overcome, and a great deal of geolocated occurrence data, organism trait data, historic documents and media are now available for reuse.
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.
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
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.
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 RIf 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 contactRenata Rawlings-Goss at email@example.com
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
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: