In conjunction with the IEEE
International Parallel and Distributed Processing SymposiumQuick Links
- HiCOMB
2019 Advance Program (click here)
- Submission Site (EasyChair)
- Online Proceedings of HiCOMB workshops
- Online Registration
Keynote and Invited Speakers
Keynote Speaker:
Ajay Royyuru
IBM Fellow; Vice President, Healthcare & Life Sciences Research,
Thomas J. Watson Research Center, Yorktown Heights, NY USA
Title: Data + AI = Insights in Biology & Medicine
Abstract: Biology and medicine are transformed into information science, enabling rapid translation from discovery to practice. This talk will present the opportunities and insights from the aggregation of electronic medical records, medical insurance claims, digital phenotype including imaging, medical literature and omics data. Such data coupled with advances in artificial intelligence techniques and detailed mechanistic models of biochemical and physiological phenomena is yielding novel insights across healthcare and life sciences. Examples from recent work in oncology, cardiology, and neuroscience will be discussed.
Biography: Ajay Royyuru leads Healthcare & Life Sciences Research at IBM. His team is actively pursuing high quality science, developing novel technologies and achieving translational insights across this industry, including areas of cancer, cardiac, neurological, mental health, immune system, and infectious diseases. Scientific interests and active projects include genomics, protein science, systems biology, computational neuroscience, health informatics, miniaturizing for medical devices, and nano-biotechnology. Working with institutions around the world, he is engaged in research that will advance personalized, information-based medicine.
Ajay previously led the life sciences research portfolio through the IBM Computational Biology Center. Ajay has authored numerous research publications and several patents in structural and computational biology. His work has featured in The New York Times, The Washington Post, BBC, Forbes, Scientific American, Nature Medicine and Nature news articles.
After his undergraduate and masters education in human biology and biophysics from All India Institute of Medical Sciences, New Delhi, Ajay obtained his Ph.D. in molecular biology from Tata Institute of Fundamental Research, Mumbai. He had postdoctoral training at Memorial Sloan-Kettering Cancer Center, New York and a brief stint at scientific software development before joining IBM Research.
In 2016 Ajay was named an IBM Fellow, the company's pre-eminent technical distinction. Ajay is a member of ISCB and IBM Academy of Technology. Title and abstract details will be posted soon.
Invited Speakers:
Ariful Azad, Indiana University Bloomington, USA
Title: HipMCL: A High-Performance Parallel Algorithm for Clustering Large-scale Networks
Abstract: We present HipMCL, a distributed-memory parallel algorithm for clustering large-scale networks. HipMCL parallelizes popular Markov clustering algorithm (MCL) that has been shown to be one of the most successful and widely used algorithms to cluster sequence similarity or expression networks. Despite its popularity, MCL is unable to cluster large datasets due to high running times and memory demands. HipMCL overcomes these challenges by employing novel parallel algorithms for sparse matrix-matrix multiplication, k selection and finding connected components and by utilizing hundreds of thousands of processors and hundreds of terabytes of memory available in large supercomputers. HipMCL can cluster large-scale networks 1000 times faster than the original MCL without any information loss. HipMCL can cluster a network with 70 million nodes and 68 billion edges in 2.4 hours using 2000 nodes of a Cray XC40 supercomputer, enabling unprecedented discoveries in network biology. HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license (https://bitbucket.org/azadcse/hipmcl/).
Bio: Ariful Azad is an Assistant Professor of Intelligent Systems Engineering at Indiana University Bloomington. Before joining IU, he was a Research Scientist in the Computational Research Division at Lawrence Berkeley National Laboratory. Dr. Azad obtained is Ph.D. from Purdue University and B.S. from Bangladesh University of Engineering and Technology. His research interests are in parallel graph algorithms, high performance computing, and bioinformatics.
Rayan Chikhi, Institut Pasteur, France
Title: High-performance and Single-node Genome Assembly using de Bruijn Graphs
Abstract: Many projects in genomics and metagenomics rely on high-throughput short-read sequencing. The reconstruction of genomes (de novo assembly) in such projects remains a highly challenging task, both in terms of computing power, and for obtaining high-quality results. An initial step is typically to construct a graph over a large set of overlapping short fragments, with input data in the range of hundreds of gigabases. The difficulty is inherently due to the volume of input data, which prevents the use of naive in-memory algorithms. This talk will give a flavor of some techniques that are used for both efficient and high-quality genome and metagenome assembly. These techniques revolve around the concept of de Bruijn graphs, which are now ubiquitous in bioinformatics. We will present an unified view around two components: i) BCALM2, a parallel algorithm that constructs de Bruijn graphs for very large sequencing datasets using a single computing node. BCALM2 was applied to very large genomes, such as the pine tree (20 Gbp), with order of magnitude performance improvement over distributed methods. ii) Then we will proceed with recent improvements to the Minia assembler software, which implements several modular strategies to construct high-quality assemblies from the output of BCALM2.
Bio: Rayan Chikhi leads the newly-created Sequence Bioinformatics research group at Institut Pasteur in Paris, France. He holds a PhD in computer science from ENS Rennes. After a postdoc in Medvedev group at Penn State, he joined CNRS as a permanent researcher in bioinformatics since 2014. His research ranges from fundamental data structures and algorithms, to practical DNA sequencing analysis projects, including genome and metagenome assembly.
HiCOMB 2019 Call For Papers
High-performance computing (HPC) has become an integral part of research and development in bioinformatics, computational biology, and medical and health informatics. The goal of the HiCOMB workshop is to showcase novel HPC research and technologies to solve data- and compute-intensive problems arising from all areas of computational life sciences. The workshop will feature contributed papers as well as invited talks from reputed researchers in the field.
For peer-reviewed papers, we invite authors to submit original and previously unpublished work that are at the interface between the "pillars" of modern day computational life sciences and HPC. For a submission to be considered, it should span at least one area from each of these two pillars. More specifically, we encourage submissions from all areas of biology that can benefit from HPC, and from all areas of HPC that need new development to address the class of computational problems that originate from biology.
Areas of interest within computational life sciences include (but not limited to):
- Biological sequence analysis (genome assembly, long/short read data structures, read mapping, clustering, variant analysis, error correction, genome annotation)
- Computational structural biology (protein structure, RNA structure)
- Functional genomics (transcriptomics, RNAseq/microarrays, proteomics)
- Systems biology and networks (biological network analysis, gene regulatory networks, metabolomics, molecular pathways)
- Tools for integrated multi-omics and biological databases (network construction, modeling, link inference)
- Phylogeny (phylogenetic tree reconstruction, molecular evolution)
- Microbes and microbiomes (taxonomical binning, classification, clustering, annotation)
- Biomedical health analytics and biomedical imaging (electronic health records, precision medicine, image analysis)
- Biomedical literature mining (text mining, ontology, natural language processing)
- Computational epidemiology (infectious diseases, diffusion mechanisms)
- Phenomics and precision agriculture (IoT technologies, feature extraction)
Areas of interest within HPC include (but are not limited to):
- Parallel and distributed algorithms (scalable machine learning, parallel graph/sequence analytics, combinatorial pattern matching, optimization, parallel data structures, compression)
- Data-intensive computing techniques (communication-avoiding/synchronization-reducing techniques, locality-preserving techniques, big data streaming techniques)
- Parallel architectures (multicore, manycore, CPU/GPU, FPGA, system-on-chip, hardware accelerators, energy-aware architectures, hardware/software co-design)
- Memory and storage technologies (processing-in-memory, NVRAM, burst buffers, 3D RAM, parallel/distributed I/O)
- Parallel programming models (libraries, domain specific languages, compiler/runtime systems)
- Scientific workflows (data management, data wrangling, automated workflows, productivity)
- Empirical evaluations (performance modeling, case-studies)
Submission guidelines
To submit a paper, please upload a PDF file through Easy Chair at the HiCOMB 2019 Submission Site. Submitted manuscripts may not exceed ten (10) single-spaced double-column pages using a 10-point size font on 8.5x11 inch pages (IEEE conference style), including figures, tables, and references (see IPDPS 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.
New this year: The
workshop organizers plan to invite authors of accepted HiCOMB 2019
papers to submit extended versions of their papers to a JPDC special
issue. More information about the special issue will be available
soon.
Important Dates
| Workshop
submissions due: (Final extended deadline) |
February
4, 2019 (11:59pm AoE) January
25, 2019 |
| Author notification: | February 26, 2019 |
| Final camera-ready papers due: | March 15, 2019 |
| Workshop: | May 20, 2019 |
Program Committee
- Mukul Bansal, University of Connecticut
- Mario Cannataro, University Magna Gracia of Catanzaro
- Jose Cecilia, Universidad Catolica de Murcia
- Somali Chaterji, Purdue University
- Tim Clark, University of Virginia
- Sally Ellingson, University of Kentucky
- Oliver Eulenstein, Iowa State University
- Xin Gao, King Abdullah University of Science and Technology
- Sandra Gesing, University of Notre Dame
- Imran Haque
- Fumihiko Ino, Osaka University
- Seunghwa Kang, NVIDIA
- Marghoob Mohiyuddin, Roche Sequencing Solutions
- Fahad Saeed, Florida International University
- Bertil Schmidt, Johannes Gutenberg University Mainz
- Mingfu Shao, Pennsylvania State University
- Hari Sundar, University of Utah
- James Taylor, Johns Hopkins University
- Joshua Welch, University of Michigan
- Bojian Xu, Eastern Washington University
- Jae-Seung Yeom, Lawrence Livermore National Laboratory
- Wenjin Zhou, University of Massachusetts Lowell
Program Chair
- Kamesh Madduri
Pennsylvania State University
Email: madduri@cse.psu.edu
General Chairs
- Alba Cristina M. A. de Melo,
Department of Computer Science, University of Brasilia
Email: alves@unb.br
- Ananth Kalyanaraman, School
of EECS, Washington State University
Email: ananth@wsu.edu
Steering Committee Members
- David A. Bader, College of Computing, Georgia Institute of Technology
- Srinivas Aluru, College of Computing, Georgia Institute of Technology