CCS² 2017
1st Annual Commonwealth Computational Summit

Commonwealth Computational Summit - W.T. Young Library Auditorium, University of Kentucky

October 17, 2017

Below you will find the initial agenda for the 1st Annual Commonwealth Computational Summit. Stay tuned for more details on speakers and topics for the Parallel Session as well as details on our keynote talks (title, abstract, bios). It should also be noted that we plan to make all speaker slides available (PDFs) after the summit in our online proceeding via this webpage.

Featured Speakers

Dr. Pascal Hitzler

Title: A Brief Introduction to Semantic Web - and a Contribution to Explainable Artificial Intelligence

Abstract: Semantic Web as a field of research and applications is concerned with methods and tools for data sharing, discovery, integration, and reuse, both on and off the World Wide Web. In the form of knowledge graphs and their underlying schemas, Semantic Web technologies are currently entering industrial mainstream. At the same time, the ever increasing prevalence of publicly available structured data on the Semantic Web enables new applications in a variety of domains, and as part of this presentation, we provide a conceptual approach that leverages such data in order to explain the input-output behavior of trained artificial neural networks. We apply existing Semantic Web technologies in order to provide an experimental proof of concept.

Mini-bio: Pascal Hitzler is endowed NCR Distinguished Professor and Director of Data Science at the Department of Computer Science and Engineering at Wright State University in Dayton, Ohio, U.S.A. His research record lists over 350 publications in such diverse areas as semantic web, neural-symbolic integration, knowledge representation and reasoning, machine learning, denotational semantics, and set-theoretic topology. He is Editor-in-chief of the Semantic Web journal by IOS Press - the leading journal in the field - and of the IOS Press book series Studies on the Semantic Web. He is co-author of the W3C Recommendation OWL 2 Primer, and of the book Foundations of Semantic Web Technologies by CRC Press, 2010 which was named as one out of seven Outstanding Academic Titles 2010 in Information and Computer Science by the American Library Association's Choice Magazine, and has translations into German and Chinese. He is on the editorial board of several journals and book series and is a founding steering committee member of the Web Reasoning and Rule Systems (RR) conference series, of the Neural-Symbolic Learning and Reasoning (NeSy) workshop series, and of the Association for Ontology Design and Patterns (ODPA). He also frequently acts as conference chair in various functions. For more information, see http://www.pascal-hitzler.de.

Dr. Chaitan Baru

Abstract: Harnessing Data for 21st Century Science and Engineering (aka Harnessing the Data Revolution, HDR) is one of NSF's six "Big Research Ideas," aimed at supporting fundamental research in data science and engineering; developing a cohesive, federated approach to the research data infrastructure needed to power this revolution; and developing of a 21st-century data-capable workforce. HDR will enable new modes of data-driven discovery allowing researchers to ask and answer new questions in frontier science and engineering, generate new knowledge and understanding by working with domain experts, and accelerate discovery and innovation. This initiative builds on NSF's history of data science investments. The HDR Big Idea is particularly well-suited for collaborations and partnerships with industry. After providing an overview of HDR, we will explore areas for potential collaboration and partnership with industry. As the only federal agency supporting all fields of science and engineering, NSF is uniquely positioned to help ensure that our country's future is one enriched and improved by data.

Chaitan Baru, PhD, is Senior Advisor for Data Science in the Computer and Information Science and Engineering Directorate at the US National Science Foundation. He co-chairs the NSF working group on Harnessing the Data Revolution Big Idea; serves as advisor to the NSF Big Data Regional Innovation Hubs and Spokes program (BD Hubs/Spokes); manages the cross-Foundation NSF BIGDATA program; and, is a member of the NSF Transdisciplinary Research in Principles of Data Science (TRIPODS) program. He also co-chairs the Big Data Inter-agency Working Group of the Networking and IT R&D program (NITRD) of the White House Office of Science and Technology Policy. He is one of the primary co-authors of the Federal Big Data R&D Strategic Plan, released May 2016. He is also a member of the NITRD Data Science Interagency Working Group and represents NSF on the Federal Data Cabinet. He was General Chair for the 33rd IEEE International Conference on Data Engineering (ICDE 2017) held on April 19-22, 2017, in San Diego, California. He is on assignment at NSF from the San Diego Supercomputer Center, University of California San Diego, where he is Associate Director for Data Initiatives and directs the Center for Large-scale Data Systems Research (clds.sdsc.edu) and the Advanced Cyberinfrastructure Development Group (acid.sdsc.edu). He has a BTech in Electronics Engineering from IIT Madras and an M.E. and PhD in Electrical Engineering from the University of Florida.

Jeffrey Kirk

Abstract: HPC and AI: Perfect Partners for Leading-edge Discovery and Innovation. High performance computing has matured into an indispensable tool for not only academic research and government labs and agencies, but also for many industry sectors: energy, manufacturing, healthcare, financial services, even digital content creation. More recently, the advent of Big Data has enabled the use of HPC techniques for large scale data analysis, expanding the scope of HPC and the reach of it into more research and enterprise use cases. Since 2012, a new regime of data-driven analytics, deep learning, has erupted in popularity, fueled by both the massive performance increases in HPC technologies and in the explosive rate of digital data being generated, collected and managed. While data analytics, including deep learning, will never eliminate the need for HPC-enabled simulations in research, the emergence of deep learning will enable both researchers and enterprises to accomplish discovery and innovation in new ways and in ways that complement, extend, and sometimes even substitute for more traditional HPC simulation techniques. Together, HPC and AI will enable the transformation for science to continue, and a new explosion in enterprise and consumer applications.

Mini Bio: Jeffrey Kirk has spent his career working at the leading edge of compute and networking. Jeff is currently working in the Server Office of the CTO as the HPC and AI Technology Strategist where he has helped grow the HPC program with a new vision, strategy, business development efforts, and new partnerships and solutions. He is now also one of the leaders of AI strategy development for Dell EMC, and is working on new AI solutions. Prior to joining Dell EMC, Jeff worked at several cutting edge semi-conductor companies. At AMD he specialized in superscalar RISC and x86 platforms for high performance computation (1999). At Mellanox he worked on some of the first Infiniband HPC installations, including the Virginia Tech cluster that reached number three on the top 500 (Big Mac) using Apple workstations (2004). While at Mellanox he supported Dr. D.K. Panda and the first implementation of MVAPICH at his alma mater, The Ohio State University. Later at Solarflare his focus was OnLoad technology and financial markets (2010). After moving to Dell, Jeff worked in Dell Networking implementing their first Fibre Channel over Ethernet systems and he holds several patents on FCoE (2013). His interest in supercomputing was sparked while working on the number three Virginia Tech cluster, but his interest in data science is encouraged and fueled by his daughter a PhD Statistician Data Scientist at the FDA.

Parallel Session Speakers

Big Data & Discovery

Big Computing/HPC

Future Technologies