Keynote 1

Scaling Graph Computations for Parallel Computational Biology

Ananth Kalyanaraman
(Washington State University)

Graph-theoretic abstractions are used in a wide variety of scientific applications as a way to model and analyze relational data. Computational biology, and more broadly the life sciences domain, has served as one of the top beneficiaries of such abstractions. In this talk, I will describe several of our efforts and related works over the years in both modeling and analyzing graph structures and computations for computational biology applications – ranging from genome assembly to protein family identification and epidemic modeling. Not only have these applications provided new ways to benefit from traditional graph computation operations, but they have also provided us new types of graphs to work with that result from the combination of relational and sequence data. Consequently, scaling these graph computations have provided additional challenges (and opportunities). We will describe a set of such graph abstractions and parallel algorithms and their applications on large-scale distributed memory supercomputers and emerging architectural paradigms. With continued breakthroughs in high-throughput sequencing technologies, as well as increased adoption of high-throughput technologies across all strata of life sciences, the importance of scalable graph methods and libraries is likely to only increase. 
Mobirise

Ananth Kalyanaraman is a Professor, Boeing Centennial Chair, and Interim Director at the School of Electrical Engineering and Computer Science, Washington State University in Pullman, WA. He is the lead PI and the Director of the USDA NIFA AgAID AI Institute for Transforming Workforce and Decision Support in agriculture. He holds a joint appointment at Pacific Northwest National Laboratory (PNNL), and affiliate faculty positions at the Molecular Plant Sciences Graduate Program and the Paul G. Allen School for Global Health. Ananth received Ph.D. (Computer Engineering, 2006) from Iowa State University.
Ananth works at the intersections of parallel computing, graph analytics, and bioinformatics and computational biology, and at the intersections of data science and AI/machine learning for real-world applications. His focus is on developing algorithms and software to support scalable analysis and modeling in large-scale data domains, particularly those in life sciences. Ananth is a recipient of U.S. Department of Energy Early Career Research Award, and several of his papers have received conference best paper awards and a prestigious graph challenge award. Ananth serves as the Editor-in-Chief for the Journal of Parallel and Distributed Computing (JPDC), and he is also a Vice-Chair for the IEEE Technical Committee on Parallel Programming (TCPP), and for the ACM SIGBIO. He also serves on the editorial board for Parallel Computing and IEEE Transactions on Computational Biology and Bioinformatics. Ananth is a senior member of IEEE, and a member of ACM and SIAM. 

Keynote 2

The Neo4j graph platform

Hannes Voigt
(Neo4j)

This talk will provide an overview of the Neo4j graph platform and how it addresses main technology trends and drives innovation, with a particular emphasis on its relevance to academic research and data-intensive disciplines. The talk will explore how the graph data model and Neo4j’s native processing capabilities offer unique advantages for uncovering complex relationships and structures in data. A central focus will be put on the platform’s graph analytics capabilities — highlighting tools and methodologies that support advanced querying, pattern detection, and knowledge discovery across diverse domains such as bioinformatics, digital humanities, and social network analysis.
Mobirise

Hannes Voigt is a Staff Engineer at Neo4j, a Ph.D. graduate from the Technische Universität Dresden, the chair of INCITS/Data Management/Expert Group on GQL, and a member of ISO/IEC JTC 1/SC 32/WG3. At Neo4j, he works in standardization and design for the property graph query languages GQL and Cypher. His research interests are in property graph schema and graph analytics. He co-authored Industrial Track Best Paper at SIGMOD 2023, the CACM title page article "The Future Is Big Graphs", and one book. He also was a speaker at the SIGMOD 2024 panel on "The Future of Graph Analytics".

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