World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
65
Citations
14894
World Ranking
2481
National Ranking
1241

Research.com Recognitions

  • 2020 - ACM Fellow For contributions to high-performance parallel and distributed computing and computational science
  • 2014 - ACM Distinguished Member
  • 2012 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2011 - IEEE Fellow For contributions to parallel and distributed computing
  • 2007 - ACM Senior Member

Overview

Manish Parashar is affiliated with the University of Utah in the United States and has an extensive research profile in computer science, particularly focusing on parallel and distributed computing systems.

Their recent published papers include:

  • ADIOS 2: The Adaptable Input Output System. A framework for high-performance data management, 2020, SoftwareX
  • A terminology for in situ visualization and analysis systems, 2020, The International Journal of High Performance Computing Applications
  • RES: Real-Time Video Stream Analytics Using Edge Enhanced Clouds, 2020, IEEE Transactions on Cloud Computing
  • A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed Computing Core Topics for Undergraduates, 2024, HAL (Le Centre pour la Communication Scientifique Directe)

Frequent co-authors collaborating with Manish Parashar include:

  • D.J. Allstot
  • John Baillieul
  • Gert Cauwenberghs
  • Michael Fang
  • Georges Gielen

The primary venues for their publications are:

  • Proceedings of the IEEE
  • arXiv (Cornell University)
  • Computing in Science & Engineering
  • Computer
  • IEEE Transactions on Parallel and Distributed Systems

Manish Parashar's research spans the main field of Computer Science, with a focus on several subfields:

  • Computer Networks and Communications
  • Information Systems and Management
  • Information Systems
  • Artificial Intelligence
  • Hardware and Architecture

The core topics covered in their work include:

  • Scientific Computing and Data Management
  • Distributed and Parallel Computing Systems
  • Cloud Computing and Resource Management
  • Advanced Data Storage Technologies
  • IoT and Edge/Fog Computing
  • Research Data Management Practices
  • Parallel Computing and Optimization Techniques

Manish Parashar has received multiple awards throughout their career:

  • ACM Fellow (2020) for contributions to high-performance parallel and distributed computing and computational science
  • ACM Distinguished Member (2014)
  • Fellow of the American Association for the Advancement of Science (AAAS) (2012)
  • IEEE Fellow (2011) for contributions to parallel and distributed computing
  • ACM Senior Member (2007)

Best Publications

  • Autonomic computing: an overview

    Manish Parashar;Salim Hariri

  • DataSpaces: an interaction and coordination framework for coupled simulation workflows

    Ciprian Docan;Manish Parashar;Scott Klasky

  • Mobility-Aware Application Scheduling in Fog Computing

    Luiz F. Bittencourt;Javier Diaz-Montes;Rajkumar Buyya;Omer F. Rana

  • A Peer-to-Peer Approach to Web Service Discovery

    Cristina Schmidt;Manish Parashar

  • Latency Performance of SOAP Implementations

    D. Davis;M.P. Parashar

  • Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence

    Nathan Baker;Frank Alexander;Timo Bremer;Aric Hagberg

  • Hello ADIOS: the challenges and lessons of developing leadership class I/O frameworks

    Qing Liu;Jeremy Logan;Yuan Tian;Hasan Abbasi

  • Cloud federation in a layered service model

    David Villegas;Norman Bobroff;Ivan Rodero;Javier Delgado

  • Flexible information discovery in decentralized distributed systems

    C. Schmidt;M. Parashar

  • PreDatA – preparatory data analytics on peta-scale machines

    Fang Zheng;Hasan Abbasi;Ciprian Docan;Jay Lofstead

  • Combining in-situ and in-transit processing to enable extreme-scale scientific analysis

    Janine C. Bennett;Hasan Abbasi;Peer-Timo Bremer;Ray Grout

  • Towards autonomic workload provisioning for enterprise Grids and clouds

    Andres Quiroz;Hyunjoo Kim;Manish Parashar;Nathan Gnanasambandam

  • A component-based programming model for autonomic applications

    Hua Liu;M. Parashar;S. Hariri

  • A concise introduction to autonomic computing

    Roy Sterritt;Manish Parashar;Huaglory Tianfield;Rainer Unland

  • The future of scientific workflows

    Ewa Deelman;Tom Peterka;Ilkay Altintas;Christopher D. Carothers

  • Enabling flexible queries with guarantees in P2P systems

    C. Schmidt;M. Parashar

  • ADIOS 2: The Adaptable Input Output System. A framework for high-performance data management

    William F. Godoy;Norbert Podhorszki;Ruonan Wang;Chuck Atkins

  • The Autonomic Computing Paradigm

    Salim Hariri;Bithika Khargharia;Houping Chen;Jingmei Yang

  • Autonomic Computing : Concepts, Infrastructure, and Applications

    Manish Parashar;Salim A Hariri

  • On partitioning dynamic adaptive grid hierarchies

    M. Parashar;J.C. Browne

  • Dynamic context-aware access control for grid applications

    G. Zhang;Manish Parashar

  • AutoMate: enabling autonomic applications on the grid

    M. Agarwal;V. Bhat;H. Liu;V. Matossian

Frequent Co-Authors

Scott Klasky
Scott Klasky Oak Ridge National Laboratory
Salim Hariri
Salim Hariri University of Arizona
Omer F. Rana
Omer F. Rana Cardiff University
Norbert Podhorszki
Norbert Podhorszki Oak Ridge National Laboratory
Mary F. Wheeler
Mary F. Wheeler The University of Texas at Austin
Xiaolin Li
Xiaolin Li Pacific Northwest National Laboratory
Geoffrey C. Fox
Geoffrey C. Fox University of Virginia
Karsten Schwan
Karsten Schwan Georgia Institute of Technology
Jacqueline H. Chen
Jacqueline H. Chen Sandia National Laboratories
Shantenu Jha
Shantenu Jha Rutgers, The State University of New Jersey

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