Who We Are
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Our Proficient Team
After earning his Master’s Degree in Physics and a Master’s Degree in Electronics & Communication Engineering, he started his extensive Research and Development career at AT&T Bell Laboratories at Holmdel, New Jersey. The focal point of his research has always been in the field of advanced networking communication and cryptography. He participated in co-authoring Request for Proposals for Internet Engineering Tasks Force. His extensive research experience led contributed to the field of Random Byte Shuffling (RBS) data storage techniques. RBS transforms the digital information into a collection of random number of bytes. A hacker may steal users data files, but the hacker will not be able to extract any information out of the random shuffled bytes.
Dr. Aater Suleman
Dr. Aater Suleman having a Ph.D. in High Performance Computing has supervised the development efforts and then the successful implementation of BOLO’s Core Technology, “Random Byte Shuffling” (RBS) Technology. He is a Technology Council Contributor for Forbes. Dr. Suleman actively puts his background in technology and experience as a professor at the University of Texas at Austin to work on designing systems for continuous improvement at the world’s leading brand
Dr. Saeed Iqbal
Dr. Saeed Iqbal holds a Doctor’s Degree in High Performance Computing (HPC) from the University of Texas at Austin. He is currently affiliated with Samsung to supervise the integration and performance analysis of the GPUs in the HPC clustering solution. He is also the lead engineer of the HPC Advisor online tool at Dell.com/hpc. This tool is used by HPC customers to design HPC clusters and associated high performance parallel storage clusters. Previously, he led the Virtualization Solutions Advisor, Beowulf HPC clustering software projects and Dell Grid Computing pilot projects. His interests include, performance modeling and analysis of parallel and distributed architectures, economic and power-efficient system design of HPC clusters, application specific compute architectures, high dimension optimization algorithms, DSP, neural nets, genetic algorithms, process scheduling models, load balancing, and resource management and optimization.