[hpc-announce] PDADS 2024 Deadline is June 2, 2024.

[Seal, Sudip]

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CALL FOR PAPERS
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The Fourth International Workshop on Parallel and Distributed Algorithms for Decision Sciences (PDADS-2024)
Date: August 12, 2024
Location: Gotland, Sweden
URL: https://urldefense.us/v3/__https://www.csm.ornl.gov/workshops/PDADS2024/index.html__;!!G_uCfscf7eWS!dlteLMXSACmd50272cHcydmNjcBhex6L0Wd5doF7gUJ9bFZgCwJ3y2isQBz5iZJ2-lsEZlhc9QABrnWuoUTAIPI$ 

PDADS will be co-hosted with the 53rd International Conference on Parallel Processing (ICPP 2024), August 12-15, 2024.

** A Journal Special Issue in under planning where selected papers accepted in the workshop will be invited for submission of extended versions. Look out for updates in the Announcements section of the workshop URL. **

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IMPORTANT DATES
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* Full Paper Submission Deadline: June 2, 2024
* Author Notification: June 16, 2024
* Camera-Ready Copy: June 26, 2024
* Workshop: August 12, 2024

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TOPICS OF INTEREST & PAPER SUBMISSION INFORMATION
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PDADS 2024 will focus on R&D efforts in cross-cutting areas at the intersection of algorithms research, computational sciences, decision sciences and optimization. The workshop will discuss latest trends and identify technology gaps in high-performance decision sciences and combinatorial optimization technologies for extant and next-generation scientific, engineering and other applications. The workshop adopts an inclusive definition of the sciences that includes the social sciences, behavioral sciences or others.

Both regular papers as well as short position papers describing work-in-progress with innovative ideas related to the workshop topics are being solicited. Accepted papers will be published by the ACM International Conference Proceedings Series (ICPS), in conjunction with those of other ICPP workshops, in a volume entitled 53rd International Conference on Parallel Processing Workshops (ICPP 2024 Workshops). This volume will be available for download via the ACM Digital Library. For paper submission guidelines, visit: https://urldefense.us/v3/__https://www.csm.ornl.gov/workshops/PDADS2024/call_for_papers.html__;!!G_uCfscf7eWS!dlteLMXSACmd50272cHcydmNjcBhex6L0Wd5doF7gUJ9bFZgCwJ3y2isQBz5iZJ2-lsEZlhc9QABrnWuC3WbVn4$ 
Topics of interest include, but are not limited to:

  *   Novel AI applications for systems and decision sciences on parallel computing systems.
  *   Deep learning solutions to optimization problems, such as reinforcement learning for control, combinatorial optimization, and decision making.
  *   Generative AI approaches to scenario analysis for decision-making support
  *   Scalable data driven decision-making methods and algorithms powered by machine learning.
  *   Deep learning model deployment and performance evaluation in decision support environments.
  *   Decision support foundation models that leverage large language models (LLMs) or are trained from scratch.
  *   Optimization techniques in machine learning, such as high-performance first and higher order iterative optimization algorithms for minimizing loss and optimizing weight and bias tensors.
  *   Application-centric manuscripts involving optimizations for decision-making capabilities in systems such as logistics, transportation and urban planning, public health, manufacturing, energy (e.g., electric grids), digital twin systems (e.g., precision agriculture, smart cities, earth systems) operations management, finance and other areas are especially encouraged.
  *   High-performance algorithms for integer/mixed-integer programming, linear/nonlinear programming, stochastic programming, robust optimization, combinatorial optimization, feasibility problems (SAT, CP, etc.).
  *   High-performance heuristic and meta-heuristic algorithms.
  *   High-performance local and complete search methods.
  *   Learning approaches for optimization in parallel and distributed environments.
  *   Parallel and distributed approaches for parameter tuning, simulation-based optimization, and black box optimization.
  *   Parallel algorithm portfolios.
  *   Quantum optimization algorithms.
  *   Use of randomization techniques for scalable decision support systems.
  *   Application of decision support systems on novel computing platforms (shared/distributed memory, edge devices, cloud platforms, field programable gate arrays, GPU, TPU, quantum computers, etc.).
  *   Use of parallel and distributed computing for timely and/or higher quality decision support.
  *   Theoretical analysis of convergence and/or complexity of parallel optimization algorithms and decision support systems.
  *   High-performance deep learning solutions that harness various parallelisms in deep learning computing for scalable acceleration on GPU/TPU.

For additional queries, email: Sudip Seal <sealsk[at]ornl[dot]gov> or Yan Liu <yanliu[at]ornl[dot]gov>
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