Ms. Asma Rehman | ML in Chemistry | AI & Machine Learning Award

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Ms. Asma Rehman | ML in Chemistry | AI & Machine Learning Award 

Accomplished Researcher, University of Agriculture Fasilabad, Pakistan

Ms. Asma Rehman is an accomplished researcher and academic from Pakistan, currently affiliated with the University of Agriculture Faisalabad, where she specializes in Green Chemistry, Organocatalysis, Polymer Degradation, and Artificial Intelligence Applications in Chemical Process Modeling.  Ms. Rehman’s research interests are strongly interdisciplinary, focusing on the integration of Artificial Intelligence and Data Science techniques with Green Chemistry principles to develop predictive models for catalytic reactions, optimize degradation processes of polymers like polystyrene, and design sustainable, energy-efficient pathways for environmental remediation. Her scientific vision aims to utilize machine learning algorithms for reaction kinetics modeling and to establish scalable frameworks for waste management, industrial pollution control, and material upcycling. In her academic journey, she has co-authored several peer-reviewed journal articles published in globally recognized outlets such as RSC Advances and AI (MDPI), highlighting her contributions to both computational and experimental chemistry. Her major research contributions include AI-assisted photocatalysis for wastewater treatment, organocatalyst design for green synthetic chemistry, and studies combining photoredox catalysis with sustainable material science. Ms. Rehman’s technical proficiency encompasses a diverse range of research skills, including statistical data analysis, reaction kinetics modeling, computational chemistry tools, spectral characterization, and data-driven optimization frameworks. Her ongoing work focuses on merging Bayesian inference models and supervised machine learning for predictive chemical engineering applications, reflecting her capability to adapt AI tools within the scientific process. She has been recognized within her institution for her academic excellence and research initiative, mentoring undergraduates and participating in interdisciplinary academic forums that align with the United Nations Sustainable Development Goals (SDGs).

Profile: ORCID

Featured Publications

  • Rehman, A., Iqbal, M. A., Haider, M. T., & Majeed, A. (2025). Artificial intelligence-guided supervised learning models for photocatalysis in wastewater treatment. AI, 6(10), Article 0258. Citations: 6

  • Ahad, A., Majeed, A., Zafar, A., Iqbal, M. A., Ali, S., Batool, M., Rehman, A., & Manzoor, F. (2025). A green marriage: The union of theophylline’s catalytic activity and healing potential. RSC Advances, 15(9), 8479A. Citations: 10

  • Manzoor, F., Majeed, A., Ibrahim, A. H., Iqbal, M. A., Rehman, A., Aziz, S., Shahzadi, A., Fatima, S., Ejaz, S., & Zafar, M. S. (2025). Nickel-photoredox catalysis: Merging photons with metal catalysts for organic synthesis. RSC Advances, 15(12), 4650E. Citations: 8

  • Rehman, A., Iqbal, M. A., & Majeed, A. (2025). Machine learning-assisted modeling of polystyrene degradation using green catalysts for sustainable waste valorization. Journal of Environmental Chemical Engineering, 13(7). Citations: 5

  • Rehman, A., Manzoor, F., & Majeed, A. (2025). Data-driven optimization of organocatalytic pathways for eco-friendly polymer processing. Green Chemistry Letters and Reviews, 18(4). Citations: 4

Mr. Ankush Sharma | Quantile Life Prediction | Young Researcher Award

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Mr. Ankush Sharma | Quantile Life Prediction | Young Researcher Award 

Emerging Research Scholar, Banaras Hindu University, India

Mr. Ankush Sharma is a dynamic and emerging research scholar in the domain of Statistics, specializing in Survival Analysis, Reliability Engineering, Degradation Modeling, Bayesian Estimation, and Functional Modeling. He is currently pursuing his Ph.D. in Statistics from Banaras Hindu University, Varanasi, India, where his research focuses on Statistical Modeling and Experimental Designs Planning for Highly Reliable Products under the supervision of Prof. Sanjeev Kumar. He has contributed actively to the global research community through publications in reputed Scopus and SCI-indexed journals and has served as a reviewer for distinguished journals such as the International Journal of Quality & Reliability Management and the Asia Pacific Prognostics and Health Management Conference. His research interests include the design of experiments for high-reliability systems, stochastic degradation modeling, and Bayesian hierarchical analysis for predictive maintenance and reliability forecasting.  His published work demonstrates his capacity for innovation and rigor, as seen in his research on thermal damage modeling, accelerated degradation testing, and stochastic EM approaches for reliability prediction. With a clear vision toward academic and research excellence, Mr. Ankush Sharma continues to contribute meaningfully to the statistical sciences community through teaching assistance, peer reviewing, and mentoring junior researchers. His professional trajectory, marked by academic distinction, research innovation, and scientific integrity, positions him as a promising scholar and future academic leader in applied statistics and reliability research.

Profile: Google Scholar | ORCID

Featured Publications

  • Sharma, A. (2025). Determination of Thermal Damage and Failure Time Analysis in Rocks Using Stochastic Models. Quality Reliability Engineering International, 2 citations.

  • Sharma, A., Tomer, S. K., & Panwar, M. S. (2025). Optimal Plans for Accelerated Destructive Degradation Tests with Stress Interaction Effects. Manuscript under review.

  • Sharma, A., & Tomer, S. K. (2025). Modeling Degradation Processes with Covariate-Dependent Random Initiation: A Stochastic EM Approach with Application to Rock Mechanics. Manuscript under review.

  • Sharma, A., & Tomer, S. K. (2025). Survival Adjusted Sequential Bayesian Experimental Designs for Degradation Models. Manuscript under review.

Mr. Iftikhar ud Din | Applied Electromagnetics | Young Researcher Award

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Mr. Iftikhar ud Din | Applied Electromagnetics | Young Researcher Award

Accomplished Researcher, University of Quebec at Trois-Rivieres, Canada

Mr. Iftikhar ud Din is an accomplished researcher and academic specializing in Telecommunication Engineering, with expertise in antenna design, applied electromagnetics, microwave and millimeter-wave systems, and metamaterial-based biosensors. He is currently pursuing his Ph.D. at the University of Quebec at Trois-Rivières (UQTR), Canada, focusing on the design and prototyping of reconfigurable intelligent surface (RIS)-aided communication systems, a cutting-edge area driving advancements in 6G networks. He holds a Master’s in Telecommunication Engineering and a B.Sc. (Hons.) in Telecommunication Engineering from the University of Engineering and Technology (UET), Peshawar, Pakistan, where his theses focused on metasurface-based 5G antennas and ultra-wideband circular monopole antennas. Professionally, Mr. Iftikhar is associated with the Electromagnetic and Antenna Research Group (EMARG) at UET Mardan, contributing to the design and analysis of high-gain antennas for sub-6 GHz and millimeter-wave spectrums. His research interests include reconfigurable intelligent surfaces (RIS), metamaterial-based antenna systems, terahertz nano-biosensors, and electromagnetic sensing for biomedical and communication applications, integrating AI-based simulation and optimization approaches. His research skills encompass electromagnetic simulation, antenna miniaturization, high-frequency modeling, and metamaterial design for next-generation sensors and communication systems. With 26 Scopus-indexed publications and 351 citations by 253 documents (Scopus ID: 57222105830), his work has been featured in prestigious journals such as IEEE Sensors Journal, IEEE Photonics Journal, Journal of Infrared, Millimeter, and Terahertz Waves, and PLOS ONE.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

  • Iftikhar, U. D., Abbasi, N. A., Ullah, W., Ullah, S., Ouameur, M. A., & Jayakody, D. N. K. (2024). A novel and compact metamaterial‐based four‐element MIMO antenna system for millimeter‐wave wireless applications with enhanced isolation. International Journal of Antennas and Propagation, 2024(1), 7480655. (7 citations)

  • Hamza, M. N., Islam, M. T., Lavadiya, S., Iftikhar, U. D., Sanches, B., Koziel, S., & Naqvi, S. I. (2025). Design and validation of ultra-compact metamaterial-based biosensor for non-invasive cervical cancer diagnosis in terahertz regime. PLOS ONE, 20(2), e0311431. (6 citations)

  • Hamza, M. N., Islam, M. T., Lavadiya, S., Iftikhar, U. D., Sanches, B., Koziel, S., & Naqvi, S. I. (2025). Ultra-compact quintuple-band terahertz metamaterial biosensor for enhanced blood cancer diagnostics. PLOS ONE, 20(1), e0313874. (20 citations)

  • Abbasi, N. A., Virdee, B., Iftikhar, U. D., Ullah, S., Althuwayb, A. A., Rashid, N., & Soruri, M. (2025). High-isolation array antenna design for 5G mm-wave MIMO applications. Journal of Infrared, Millimeter, and Terahertz Waves, 46(1), 12. (12 citations)

Dr. Prashant Kapil | AI and Natural Language Processing | Best Researcher Award – 2243

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Dr. Prashant Kapil | AI and Natural Language Processing | Best Researcher Award 

Distinguished Academic and Researcher, Bennett University, India

Dr. Prashant Kapil is a dedicated researcher and academic recognized for his expertise in Artificial Intelligence (AI) and Natural Language Processing (NLP), focusing on hate speech detection, cross-lingual learning, and ethical AI for social media safety. He earned his Ph.D. in Computer Science and Engineering from the Indian Institute of Technology Patna,  Dr. Kapil also holds an M.E. in Software Engineering from Jadavpur University and a B.Tech in Information Technology from the West Bengal University of Technology, forming a strong academic foundation in computational systems and intelligent algorithms. Professionally, he serves as an Assistant Professor at Bennett University (The Times Group), where he teaches and supervises research in machine learning, NLP, and AI ethics. His Scopus ID is 57219354937, with 157 citations from 154 documents, 4 indexed publications, and an h-index of 3, underscoring his growing research influence. His interests span multimodal and multilingual NLP, transformer-based deep learning, sentiment and emotion analysis, and AI fairness in communication technologies. Dr. Kapil possesses advanced research and analytical skills in Python programming, deep learning frameworks, and computational model development, complemented by his expertise in academic writing and data-driven experimentation. His honors include the Graduate Aptitude Test in Engineering (GATE) Scholarship, the UGC JRF-SRF Fellowship, and the Institute Ph.D. Fellowship from IIT Patna, recognizing his outstanding academic achievements.

Profile: GOOGLE SCHOLAR | SCOPUS | ORCID

Featured Publications

  • Kapil, P., & Ekbal, A. (2025). A transformer-based multi-task learning approach to multimodal hate detection. Natural Language Processing Journal, 2025. (12 citations)

  • Kapil, P., & Ekbal, A. (2023). HHSD: Hindi hate speech detection leveraging multi-task learning. IEEE Access, 2023. (45 citations)

  • Kapil, P., & Ekbal, A. (2020). A deep neural network-based multi-task learning approach to hate speech detection. Knowledge-Based Systems, 2020. (68 citations)

  • Kapil, P., & Ekbal, A. (2024). Cross-lingual zero-shot and few-shot learning for hate speech detection. SSRN Working Paper, 2024. (32 citations)