Niti Kant | Computational Theory | Best Researcher Award

Published on by Computer Science Engineering

Prof. Dr. Niti Kant | Computational Theory | Best Researcher Award

Professor | University of Allahabad | India

Prof. Dr. Niti Kant is a distinguished physicist currently serving in the Department of Physics, University of Allahabad, Prayagraj, India. With a Ph.D. from the Indian Institute of Technology (IIT) Delhi (2005) under the supervision of Dr. A. K. Sharma, his research focuses on laser–plasma interaction, self-focusing of lasers, harmonic generation, laser-induced electron acceleration, and terahertz (THz) radiation generation. Over the past two decades, Dr. Kant has made significant contributions to theoretical plasma physics, employing advanced analytical and numerical modeling approaches using Mathematica and Origin. He has published over 150 research papers in reputed international journals indexed by SCI, earning an H-index of 33 on Google Scholar, reflecting the global impact of his research. His academic journey includes postdoctoral research at POSTECH, South Korea, and academic leadership at Lovely Professional University, Punjab, where he served as Professor before joining the University of Allahabad. Dr. Kant has successfully led several sponsored research projects funded by CSIR, SERB, and DST, totaling over ₹50 lakhs, and has guided more than ten Ph.D. scholars in cutting-edge areas such as THz generation, nonlinear optics, and high-power laser–matter interaction. A life member of several prestigious scientific societies, including the Indian Science Congress Association, Optical Society of India, and Plasma Science Society of India, he also serves on editorial and review boards of international journals and as a peer reviewer for top publishers like Elsevier, IOP, and AIP. His work has been recognized with multiple honors, including the Merit Award (2024) by the University of Allahabad, Research Excellence Awards (2020, 2021), and the Outstanding Scientist Award (2020). With active international collaborations across the UK, Czech Republic, South Korea, and the USA, Dr. Kant’s research continues to advance the frontiers of laser–plasma physics, contributing to innovations in photonics, clean energy, and applied plasma technologies with profound implications for scientific and technological progress.

Featured Publication

Kamboj, O., Azad, T., Rajput, J., & Kant, N. (2025). The effect of density ramp on self-focusing of q-Gaussian laser beam in magnetized plasma. Journal of Optics (India). Citations: 2

Azad, T., Kant, N., & Kamboj, O. (2025). Efficient THz generation by Hermite–cosh–Gaussian lasers in plasma with slanting density modulation. Journal of Optics (India). Citations: 23

Singh, J., Kumar, S., Kant, N., & Rajput, J. (2025). Effect of frequency-chirped ionization laser on accelerated electron beam characteristics in plasma wakefield acceleration. European Physical Journal Plus. Citations: 1

Anshal, L., Kant, N., Azad, T., Rajput, J., & Kamboj, O. (2025). Propagation of Hermite–cosh–Gaussian laser beam in free-electron laser device under upward plasma density ramp. Laser Physics Letters. Citations: 1

Azad, T., Kant, N., & Kamboj, O. (2025). Enhanced third harmonic generation and SRS suppression in magnetized rippled plasma using Hermite cosh–Gaussian laser beam. Journal of Optics (India). Citations: 2

Prof. Dr. Niti Kant’s pioneering research in laser–plasma interaction, nonlinear optics, and terahertz generation has advanced the understanding of high-power laser applications, enabling innovations in photonics, clean energy, and next-generation communication technologies. His work bridges fundamental physics with practical technologies, fostering global scientific collaboration and contributing to sustainable technological progress.

Rana Ghazali | Data Science | Best Researcher Award

Published on by Computer Science Engineering

Dr. Rana Ghazali | Data Science | Best Researcher Award

Researcher |McMaster University | Iran

Dr. Rana Ghazali focuses on advancing intelligent computing systems through the integration of machine learning, reinforcement learning, and large language models to optimize big data and distributed computing environments. Her work bridges the domains of cloud computing, Hadoop-based systems, and intelligent caching to enhance computational performance and resource utilization in large-scale data frameworks. She has contributed to innovative algorithms such as CLQLMRS and H-SVM-LRU for improving cache locality and intelligent cache replacement in MapReduce job scheduling, combining machine learning with distributed system optimization. Rana’s research also extends to the design and analysis of routing protocols in mobile ad hoc networks, leveraging bio-inspired algorithms such as the Ant Colony Optimization method to improve network efficiency. Her current exploration includes the application of reinforcement learning in scheduling and performance enhancement for distributed computing platforms, with additional attention to emerging paradigms like edge, fog, and serverless computing. As a researcher affiliated with the Resource Allocation and Stochastic Systems Lab at McMaster University, she contributes to cutting-edge discussions on adaptive data management, cyber and network security, and intelligent system design. Rana’s expertise further encompasses data analytics, large language models, and the intersection of artificial intelligence with real-world computing challenges. She has served as a reviewer for leading international journals including Elsevier and Wiley publications and has participated in academic collaborations that explore deep learning and resource optimization in distributed architectures. Her research endeavors consistently emphasize scalable, secure, and intelligent computational systems that advance the performance of modern data-intensive applications. Rana Ghazali has 13 citations, 2 documents, and an h-index of 2.

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Ghazali, R., Down, D. G. (2025). Smart data prefetching using KNN to improve Hadoop performance. EAI Endorsed Transactions on Scalable Information Systems, 12(3). Cited by 1

Ghazali, R., Adabi, S., Rezaee, A., Down, D. G., & Movaghar, A. (2023). Hadoop-oriented SVM-LRU (H-SVM-LRU): An intelligent cache replacement algorithm to improve MapReduce performance. arXiv preprint arXiv:2309.16471. Cited by 2

Ghazali, R., Adabi, S., Rezaee, A., Down, D. G., & Movaghar, A. (2022). CLQLMRS: Improving cache locality in MapReduce job scheduler using Q-learning. Journal of Cloud Computing, 9. Cited by 9

Ghazali, R., Adabi, S., Down, D. G., & Movaghar, A. (2021). A classification of Hadoop job schedulers based on performance optimization approaches. Cluster Computing, 24(4), 3381–3403. Cited by 11

Ghazali, R., Down, D. G. (2025). A systematic overview of caching mechanisms to improve Hadoop performance. Concurrency and Computation: Practice and Experience, 37(25–26), e70337.