HPC-BOD

2nd IEEE International Workshop on High-Performance Computing and Analytics for Big Omics Data
In conjunction with IEEE BIBM 2019
November 18-21, 2019, San Diego, CA, USA

HPC-BOD 2019 Call For Papers

Enormous amounts of data are being produced using modern technologies such as Next Generation Sequencing Machines and high-throughput Mass Spectrometers. This creates problems in terms of storage, transmission and computations of these Big Data sets. In order to process such data in a timely manner, big data analytics techniques and high-performance computing is becoming an essential component in system biology, bioinformatics and computational biology.

The goal of this workshop is to provide a forum for big data analytics and high-performance computing professionals and academics alike to discuss latest research in HPC solutions to these compute-intensive and data-intensive problems. We are especially interested in parallel and distributed architectures and algorithms, cache-oblivious and out-of-core HPC algorithms, memory-efficient algorithms, large scale data mining and system biology techniques, and novel approaches for big data, cloud computing, multicores, GPUs, FPGA’s and new accelerators for biological applications.

The workshop will feature submitted papers as well as invited papers and talks from reputed researchers in the field of big data analytics, high-performance computing and computational biology. There are three basic research thrusts that the workshop would be interested (but not limited to);

Areas of interest within computational life sciences include (but not limited to):

  1. Computational Genomics and Metagenomics
    Genome assembly, long/short read data structures, read mapping, clustering, variant analysis, error correction, genome annotation, and other computational problems in large-scale genomics
  2. Computational Proteomics and Proteogenomics
    Peptide identification from Big Mass Spectrometry data including database search and denovo methods, Genome annotations via mass spectrometry, Identification of post-translational modifications, Structural genomics via mass spectrometry, Protein-protein interactions and other computational problems in large-scale proteomics
  3. Computational Neuroinformatics and Connectomics
    Standardization in multiscale and multimodal modeling, Computational infrastructure for neuroscience: automation / pipelines, Machine learning in neuroscience, Reproducible neuroscience + open science
  4. Other Omics and Integration for Systems Biology
    Other computational problems in omics including but not limited to Epigenomics, Lipidomics, Glycomics, Foodomics, Transcriptomics, Metabolomics and integration of these omics datasets to get systems biology insights are also encouraged to submit.

Areas of interest within HPC include (but are not limited to):

  1. Parallel and Distributed Algorithms
    Scalable machine learning, parallel graph/sequence analytics, combinatorial pattern matching, optimization, parallel data structures, compression/decompression
  2. Data-intensive Computing Techniques
    Communication-avoiding/synchronization-reducing techniques, locality-preserving techniques, big data streaming techniques
  3. Parallel Architectures
    Multicore, manycore, CPU/GPU, FPGA, system-on-chip, hardware accelerators, energy-aware architectures, hardware/software co-design
  4. Accessible Scientific workflows
    Data management, Data wrangling, Automated workflows, Cloud-enabled solutions for computational biology, and Energy-aware High-Performance Biological Applications

Areas of interest within Big Data Analytics include (but are not limited to):

  1. Big Data Analytics
    Novel techniques to deal with big omics data including but not limited to sketching, sampling, streaming, compression/decompression, succinct data-structures and algorithms, novel encoding techniques, efficient methods to integrate multiomics data and Multimedia and Multi-structured Omics data
  2. Hardware Acceleration for Big Data
    FPGA/CGRA/GPU accelerators for Big Data applications, Domain-specific and heterogeneous architectures, and design that can accelerate machine-learning aspects of dealing with big omics data.
  3. Big Data Infrastructure
    Cloud/Grid/Stream Computing for Big Data, HPC for Big Data, Design and Deployment Energy-efficient Computing for Big Data, Cloud, and Grid Computing to Support Big Data, Software Techniques and Architectures in Cloud/Grid/Stream Computing
  4. Big Data Management
    Search and Mining of variety of omics data, Algorithms and Systems for Big DataSearch, Distributed, and Peer-to-peer Search, Big Data Search Architectures, Scalability and Efficiency, Visualization Analytics for Big Data, Multimedia and Multi-structured Omics data

Submission Guidelines:

To submit a paper, please upload a PDF file through submission site at IEEE HPC-BOD submission site. Submitted manuscripts may not exceed ten (8) single-spaced double-column pages using a 10-point size font on 8.5×11 inch pages (IEEE conference style), including figures, tables, and references (see IEEE BIBM Call for Papers for more details). All papers will be reviewed. Proceedings of the workshops will be distributed at the conference and are submitted for inclusion in the IEEE Explore Digital Library after the conference.

Important Dates:

Sep 30, 2019: Due date for full workshop papers submission
Oct 15, 2019: Notification of paper acceptance to authors
Nov 1, 2019: Camera-ready of accepted papers
Nov 18-21, 2019: Workshops

Workshop Organization

Workshop Chairs:

Fahad Saeed
School of Computing and Information Sciences
Florida International University, Miami FL 33319 USA
Email: fsaeed@fiu.edu

Ajay Gupta
Department of Computer Science
Western Michigan University, Kalamazoo MI 49008 USA
Email: ajay.gupta@wmich.edu

Publicity Chairs:
Muaaz Awan (Lawrence Berkeley National Laboratory)
Sandino Vargas-Perez (Kalamazoo College)

Program Committee

 1. Abedalrhman Alkhateeb |  University of Windsor |  Canada | 
  2. Muaaz Awan |  Lawrence Berkeley National Laboratory (Berkeley Lab) |  United States | 
  3. Mario Cannataro |  Data Analytics Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia" Catanzaro |  Italy | 
  4. Jose Cecilia |  Universidad Catolica de Murcia |  USA | 
  5. Somali Chaterji |  Purdue University |  USA | 
  6. Tim Clark |  University of Virginia |  USA | 
  7. Sally Ellingson |  University of Kentucky |  USA | 
  8. Xin Gao |  King Abdullah University of Science and Technology |  Saudi Arabia | 
  9. Fumihiko Ino |  Osaka University |  Japan | 
 10. Giri Narasimhan |  Florida International University |  United States | 
 11. Sanguthevar Rajasekaran |  University of Connecticut |  U.S.A | 
 14. Saeed Salem |  North Dakota State University |  United States | 
 15. Bertil Schmidt |  JGU Mainz |  Germany | 
 16. Ashok Srinivasan |  University of West Florida |  USA | 
 17. Bojian Xu |  Eastern Washington University |  USA | 
 18. Jae-Seung Yeom |  Lawrence Livermore National Laboratory |  USA | 
 19. Wenjin Zhou |  University of Massachusetts Lowell |  USA | 

 

Keynote Speaker(s)
TBA

 

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