Dr. Imran Rasheed’s research primarily focuses on Information Retrieval (IR) and Natural Language Processing (NLP), with special emphasis on query formulation, document ranking, and semantic matching, particularly in low-resource and morphologically rich languages. His Ph.D. work at the Indian Institute of Technology (ISM), Dhanbad, addressed key challenges in improving the effectiveness of IR systems by integrating linguistic preprocessing techniques such as stemming, stopword removal, and part-of-speech tagging, thereby enhancing search relevance and user satisfaction.

He has contributed to the development of hybrid retrieval models that combine traditional statistical approaches with emerging machine learning methods to improve document classification, clustering, and content recommendation. His research outcomes include the creation of optimized pipelines for text classification, semantic retrieval, and sentiment analysis, with a focus on underrepresented languages like Urdu.

Dr. Rasheed has published in reputed journals and presented his work at national and international conferences. Notably, he serves as an active reviewer for the International Conference on Contemporary Computing (IC3), contributing his expertise to evaluate scholarly work in the domains of AI, ML, IR, and NLP.

Beyond research publications, he actively mentors student projects and internships focused on real-world machine learning applications, such as car condition-based rating systems and introductory data analytics using Python and ML tools. His future research plans include exploring cross-lingual information retrieval, ethical and explainable AI in NLP, and intelligent systems for social and healthcare data, with an aim to secure collaborative and interdisciplinary research opportunities.