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RESEARCH PROJECTS

Funded Projects:

  1. PI, “Excellence in Research: Collaborative Research: Strengthen the Foundation of Big Data Analytics via Interdisciplinary Research among HBCUs,” $966,513, October 2018 – September 2021. Sponsored by the National Science Foundation (NSF)

  2. PI, “EAGER: A Data Flow Approach to Meet the Challenges of Big Data Analytics,” $299,999, September 2016 – August 2019. Sponsored by the National Science Foundation (NSF)

  3. PI, “PFI: AIR-TT: Developing a prototype for the next generation of petroleum data processing and analytics platform,” $212,000, September 2015 - August 2019. Sponsored by the National Science Foundation (NSF)

  4. Associate Director of Research, Co-PI: “Center of Excellence in Research and Education for Big Military Data Intelligence (CREDIT),” $5,000,000, June 2015 - May 2020, Sponsored by the Department of Defense (DoD)

  5. Co-PI: “Acquisition of Big Data Analytics Instruments for Research and Education in Big Military Data Intelligence at PVAMU,” $500,000, June 2016 - May 2019, Sponsored by the Department of Defense (DoD)

  6. Co-PI: “Big Data Analytics Research For Navy Applications,” $300,000, August 2016 - July 2019, Sponsored by the Department of Defense (DoD)

  7. PI:” I-Corps: Feasibility Study for Commercializing a Domain-Specific Big Data Analytics Cloud Software Stack”, $50,000, Dec 2014 – June 2016, Sponsored by the National Science Foundation (NSF)

  8. PI: “II-NEW: COLLABORATIVE - Image Processing Cloud (IPC): A Domain-Specific Cloud Computing Infrastructure for Research and Education,” $740,000, June 2012 – May 2016, Sponsored by the National Science Foundation (NSF)

  9. Co-PI: “MRI: Acquisition of a High Performance Computer Cluster for Multidisciplinary Computational Research at Prairie View A&M University,” $394,222, September 2012 - August 2015, Sponsored by the National Science Foundation (NSF)

  10. Co-PI: “Enriching Computing Curricula Through HPC Teaching and Research,” $399,834, June 2013 - May 2016, Sponsored by the National Science Foundation (NSF)

SEISMIC DATA ANALYTICS PLATFORM

The oil & gas industry and research community demand a scalable and productive big data analytics platform to efficiently discover the complex nature buried in the big seismic data sets in the earth exploration process. The research built a cloud-based solution to allow researchers and developers to manage, analyze and visualize huge seismic data set.     

DEEP LEARNING FOR SEISMIC INTERPRETATION

Deep Learning is a new and promising technology to unveil the valuable insights inside big data sets. The research created several deep learning models to automatically identify the geological features from seismic data sets, which was typically manually interpreted by geology professionals.

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DATA SCIENCE MEETS GEOPHYSICS

Seismic Inversion is a geophysics-based solution to reconstruct the earth subsurface image by inverting seismic data observed via the distributed sensors on the surface. The solution heavily relies on physics and numerical optimization methods to find a solution. The research is combining physics with data science to achieve the next level of performance.

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