Advances in Computers

Suyel Namasudra has received Ph.D. degree from the National Institute of Technology Silchar, Assam, India. He was a post-doctorate fellow at the International University of La Rioja (UNIR), Spain. Currently, Dr. Namasudra is working as an assistant professor in the Department of Computer Science and Engineering at the National Institute of Technology Agartala, Tripura, India. Before joining the National Institute of Technology Agartala, Dr. Namasudra was an assistant professor in the Department of Computer Science and Engineering at the National Institute of Technology Patna, Bihar, India. His research interests include blockchain technology, cloud computing, DNA computing, and information security. Dr. Namasudra has edited 7 books, 5 patents, and 85 publications in conference proceedings, book chapters, and refereed journals like IEEE TII, IEEE TCE, IEEE T-ITS, IEEE TSC, IEEE TCSS, IEEE TCBB, ACM TOMM, ACM TOSN, ACM TALLIP, FGCS, CAEE, and many more. He is the Editor-in-Chief of the Cloud Computing and Data Science (ISSN: 2737-4092 (online)) journal. Dr. Namasudra has served as a Lead Guest Editor/Guest Editor in many reputed journals like IEEE TCE (IEEE, IF: 4.3), IEEE TBD (IEEE, IF: 7.2), ACM TOMM (ACM, IF: 3.144), MONET (Springer, IF: 3.426), CAEE (Elsevier, IF: 3.818), CAIS (Springer, IF: 4.927), CMC (Tech Science Press, IF: 3.772), Sensors (MDPI, IF: 3.576), and many more. He has also participated in many international conferences as an organizer and session chair. Dr. Namasudra is a senior member of IEEE, and a member of ACM and IEI. He has been featured in the list of the top 2% scientists in the world in 2021, 2022, and 2023. His h-index is 37.

1. VLSI for Super-Computing: Creativity in R+D from Applications and Algorithms to Masks and Chips2. Bulk Bitwise Execution Model in Memory: Mechanisms, Implementation, and Evaluation3. Embracing the Laws of Physics: Three Reversible Models of Computation4. WSNs in Environmental Monitoring: Data Acquisition and Dissemination Aspects5. Energy efficient implementation of tensor operations using dataflow paradigm for machine learning6. A Run-Time Job Scheduling Algorithm for Cluster Architectures with DataFlow Accelerators