Research

Parashar is PI or Co-PI on several large-scale, federal funded research projects, covering a broad range of topics related to translational computer science and extreme-scale data. They include:

  • 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

Parashar has co-authored over 400 technical papers (largely in rigorously refereed venues) in leading journals and international conference and workshop proceedings, and has edited multiple books, conference proceedings and journal special issues. He has also given many invited presentations at various national and international venues, including over 50 keynotes and distinguished lectures. His research has led to 1 patent and 2 provisional patents. He has developed and deployed several software systems that are being used for scientists and engineering in academia and industry.

Parashar has an h-index of 63, according to his Google Scholar page.

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.

Current 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.