Prof. Parashar has made significant contributions to translational computer science, extreme-scale data management, and high-performance computing. He is a Fellow of AAAS, ACM, and IEEE. He has co-authored over 400 technical papers (typically in rigorously refereed venues) in leading journals and international conference and workshop proceedings. He has edited multiple books, conference proceedings, and special journal issues. He has also given many invited presentations at national and international venues, including over 75 keynotes and distinguished lectures worldwide. His research has led to one patent and two provisional patents.
Parashar’s notable works include contributions to structured adaptive mesh refinement (SAMR), extreme-scale data management, autonomic scientific computing, and national and regional cyberinfrastructure. He has developed and deployed several software systems that are being used by scientists and engineers in academia and industry.
Parashar has an h-index of 67, according to his Google Scholar page.
Parashar leads/co-leads several research projects, covering a broad range of topics related to translational computer science and extreme-scale data. They include:
SciDX, a comprehensive software stack built on NDP-POP (Point of Presence), empowering users to transform and utilize data in real time.
National Data Platform, or NDP, a federated and extensible data ecosystem to promote collaboration and innovation on top of existing data and cyberinfrastructure capabilities.
DataSpaces, an extreme scale data management framework
R-Pulsar, an IoT edge framework
Virtual Data Collaboratory, a regional cyberinfrastructure for collaborative data-intensive science
CometCloud, an automatic framework for dynamically federated, hybrid data infrastructure
SciDx
The Science Data Exchange (sciDX) is composable and customizable services platform underlying the National Data Platform (NDP), a federated and extensible data ecosystem to promote collaboration, innovation and equitable use of data using existing and future cyberinfrastructure capabilities. sciDX provides scalable data discovery, data staging, data streaming and in-situ data processing capabilities. It’s benefits are:
Simplified Data Management – SciDx provides a user-friendly platform that seamlessly integrates with diverse workflows and research communities.
Enhanced Collaboration – It bridges gaps in data sharing by unifying multiple data sources and tailoring compatibility to unique user needs.
Flexible and Extensible – The platform supports data from various sources, allows user-defined services, and integrates with multiple workflows.
Community-Driven Insights – SciDx hosts common analytics tools and acts as a data proxy, empowering shared insights and collaborative research.
NDP
The National Data Platform, or NDP, is a federated and extensible data ecosystem to promote collaboration, innovation, and use of data on top of existing cyberinfrastructure capabilities.
NDP is envisioned as a broad data ecosystem to enable data-enabled and AI-integrated research and education workflows.
NDP is aimed to:
Facilitate data registration, discovery and usage through a centralized hub
Enhance distributed CI capabilities through distributed points of presence
Cultivate resources for classroom education and data challenges
Assist research and learning through personalized workspaces
Autonomics for Science and Engineering
M. Parashar, S. Hariri, Autonomic Computing: An Overview, Unconventional Programming Paradigms 2004. Lecture Notes in Computer Science, vol 3566. Springer, Berlin, Heidelberg.
H. Liu*, M. Parashar and S. Hariri, A component-based programming model for autonomic applications, ICAC, 2004, https://doi.org/10.1109/ICAC.2004.1301341.
Computational Engines for Large-scale Adaptive Applications
M. Parashar, J. C. Browne, System Engineering for High Performance Computing Software: The HDDA/DAGH Infrastructure for Implementation of Parallel Structured Adaptive Mesh Refinement, IMA Volume 117, 2000, https://doi.org/10.1007/978-1-4612-1252-2_1.
M. Parashar, J. C. Browne, "On partitioning dynamic adaptive grid hierarchies," HICSS-29, 1996, https://doi.org/10.1109/HICSS.1996.495511.
J. Steensland, S. Chandra, M. Parashar, An Application-Centric Characterization of Domain-Based Inverse Space-Filling Curve Partitioners for Parallel SAMR Applications, IEEE Transactions on Parallel and Distributed Systems, Vol. 13, No. 12, 2002, https://doi.org/10.1109/TPDS.2002.1158265.
Extreme-Scale Data Discovery and Management
C. Schmidt, M. Parashar, Flexible Information Discovery in Decentralized Distributed Systems, IEEE HPDC-12, 2003, https://doi.org/10.1109/HPDC.2003.1210032.
C. Docan, M. Parashar, S. Klasky. DataSpaces: an interaction and coordination framework for coupled simulation workflows. Cluster Computing, 15, 163–181 (2012), https://doi.org/10.1007/s10586-011-0162-y.
Tong Jin, Fan Zhang, Qian Sun, Melissa Romanus, Hoang Bui, Manish Parashar, Towards autonomic data management for staging-based coupled scientific workflows, Journal of Parallel and Distributed Computing, Volume 146, 2020, Pages 35-51, https://doi.org/10.1016/j.jpdc.2020.07.002.
National and regional cyberinfrastructure
Rodero, I. and Parashar, M. (2019) ‘Data Cyber-Infrastructure for End-to-end Science: Experiences from the NSF Ocean Observatories Initiative’, Computing in Science & Engineering, pp. 1–1. doi:10.1109/MCSE.2019.2892769.
M. Parashar, "Democratizing Science Through Advanced Cyberinfrastructure," in Computer, vol. 55, no. 9, pp. 79-84, Sept. 2022, doi: 10.1109/MC.2022.3174928.
Recent Work: Urgent computing, continuum computing, intelligent data-delivery, translational computer science research
Fauvel, K., Balouek-Thomert, D., Melgar, D., Silva, P., Simonet, A., Antoniu, G., Costan, A., Masson, V., Parashar, M., Rodero, I., & Termier, A. (2020). A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 403-411. https://doi.org/10.1609/aaai.v34i01.5376.
[10] Y. Qin, I. Rodero and M. Parashar, "Toward Democratizing Access to Facilities Data: A Framework for Intelligent Data Discovery and Delivery," in Computing in Science & Engineering, vol. 24, no. 3, pp. 52-60, 1 May-June 2022, https://doi.org/10.1109/MCSE.2022.3179408.
[11] D. Abramson and M. Parashar, "Translational Research in Computer Science," in Computer, vol. 52, no. 9, pp. 16-23, Sept. 2019, https://doi.org/10.1109/MC.2019.2925650.
Open-source Software
DataSpaces (2013 R&D 100 award winner), for extreme scale coupled workflows; CometCloud (patent) for enabling workflows dynamic software defined infrastructure, Fenix for online failure recovery on extreme scale systems; R-Pulsar (provisional patent) for data-driven edge-cloud integration; and GrACE/DAGH and MACE for very large scale, dynamically adaptive and coupled simulations.