CLSAC 2025
Addressing the Large-Scale Requirements of Analytic Grand Challenges
October 6--Oct 9, 2025
Westin Annapolis
Annapolis, Maryland
Theme:
At the first Chesapeake Large Scale Analytics Conference (CLSAC) in 2012, we looked at gaps in our ability to process and understand large data. Since 2012, the field of data analytics has undergone a revolution, with the advent of ever more impressive machine-learning models that apply to a wide range of disciplines. In light of explosive growth of LLMs and Generative AI, CLSAC 2025 returns to its roots and reexamines grand challenges in analytics, with an emphasis on the large-scale requirements, which, if anything, are even more pressing than in 2012.
These challenges include such things as revolutionizing health care, understanding climate change, ensuring cybersecurity, transforming energy and exploring space. The scale and complexity of these challenges and their associated data influence every aspect of the computing spectrum: from algorithms and software development, to processor design/packaging, memory architecture, interconnect and storage, to policy on the grand scale.
The increased volume, velocity, and variety of data enable greater fidelity and understanding of complex systems, but not without significant technological, cost, legal, safety, security and privacy challenges. From massive graph analytics, integrating machine learning models to applying predictive analytics in real-time, the need for large-scale infrastructure, algorithms, and scalable computational resources is more urgent than ever.
As the focus of this year’s conference is large-scale analytics, we bring together experts from a wide array of fields to discuss solutions to problems that are beyond our current capability. The conference will explore cutting-edge technologies that not only solve today's challenges but also incorporate resilience and sustainability for the future.
At the first Chesapeake Large Scale Analytics Conference (CLSAC) in 2012, we looked at gaps in our ability to process and understand large data. Since 2012, the field of data analytics has undergone a revolution, with the advent of ever more impressive machine-learning models that apply to a wide range of disciplines. In light of explosive growth of LLMs and Generative AI, CLSAC 2025 returns to its roots and reexamines grand challenges in analytics, with an emphasis on the large-scale requirements, which, if anything, are even more pressing than in 2012.
These challenges include such things as revolutionizing health care, understanding climate change, ensuring cybersecurity, transforming energy and exploring space. The scale and complexity of these challenges and their associated data influence every aspect of the computing spectrum: from algorithms and software development, to processor design/packaging, memory architecture, interconnect and storage, to policy on the grand scale.
The increased volume, velocity, and variety of data enable greater fidelity and understanding of complex systems, but not without significant technological, cost, legal, safety, security and privacy challenges. From massive graph analytics, integrating machine learning models to applying predictive analytics in real-time, the need for large-scale infrastructure, algorithms, and scalable computational resources is more urgent than ever.
As the focus of this year’s conference is large-scale analytics, we bring together experts from a wide array of fields to discuss solutions to problems that are beyond our current capability. The conference will explore cutting-edge technologies that not only solve today's challenges but also incorporate resilience and sustainability for the future.
Organizing Committee:
Jim Ang, Pacific Northwest National Laboratory
Almadena Chtchelkanova, National Science Foundation
John Feo, Pacific Northwest National Laboratory
David Haglin, Trovares, Inc.
Laura Monroe, Los Alamos National Laboratory
Richard Murphy, Gem State Informatics, Inc.
Ron Oldfield, Sandia National Laboratories
Steve Pritchard, Committee Advisor
Tyler Simon, Department of Defense
Brad Spiers, Committee Advisor
Almadena Chtchelkanova, National Science Foundation
John Feo, Pacific Northwest National Laboratory
David Haglin, Trovares, Inc.
Laura Monroe, Los Alamos National Laboratory
Richard Murphy, Gem State Informatics, Inc.
Ron Oldfield, Sandia National Laboratories
Steve Pritchard, Committee Advisor
Tyler Simon, Department of Defense
Brad Spiers, Committee Advisor