Prof. (Dr.) AMIT SRIVASTAVA has made significant contributions to mathematical research, particularly in the fields of fuzzy sets, probability theory, and information theory. With over 45 research publications in high-impact international journals indexed in SCIE and Scopus, his work has addressed complex problems involving uncertainty, divergence measures, and belief-based reasoning frameworks. A key focus of his research lies in the generalization of negation in probability distributions and its implications in decision-making and data classification. His papers in Chaos, Solitons & Fractals, Soft Computing, and the International Journal of Intelligent Systems explore novel divergence measures, entropy formulations, and their application to real-world problems such as software bug prediction and fuzzy transportation optimization. His interdisciplinary work includes the development of information-theoretic frameworks for belief function divergence, adaptive learning models with attention-based LSTM networks, and entropy-driven models for medical diagnosis. Many of these contributions provide a mathematical backbone to modern AI and uncertainty modelling systems. Prof. Srivastava has collaborated extensively and served as co-author on several conference proceedings published by IEEE and ACM, including recent work on projection-based clustering, bug prediction using bat-inspired optimization, and dimensionality reduction techniques. He has successfully guided multiple Ph.D. scholars working on the frontiers of information measures and fuzzy logic, with applications in coding theory, decision sciences, and knowledge representation. His consistent research output, coupled with conference presentations and journal publications, underscores a strong commitment to advancing mathematical foundations for uncertainty and intelligent system modelling.