0000-0003-0247-8908 About
Professor (Dr.) Anurag Rana holds a distinguished position as both a Professor and a Research Scientist, with a profound specialisation in the rapidly evolving field of Artificial Intelligence (AI). His expertise encompasses a broad spectrum of AI disciplines, including Generative AI, which focuses on creating new content, and Agentic AI, which involves developing autonomous and intelligent agents. He is also deeply engaged with General-Purpose AI (GPAI), striving for AI systems that can perform a wide range of tasks. Further enriching his profile are his strong foundations in Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs), which are crucial for advanced natural language processing. His capabilities extend to Natural Language Processing (NLP) itself, Computer Vision (CV) for interpreting and understanding visual data, and the overarching domain of Data Science. In addition, he possesses significant knowledge in the cutting-edge areas of Quantum Computing and Soft Computing. Professor Rana's professional journey is marked by extensive experience that spans various academic and research roles, including impactful contributions to teaching, rigorous research endeavours, academic supervision of aspiring scholars, meticulous curriculum development, and the driving force behind AI-driven innovation.
The breadth of his research is truly remarkable, venturing into diverse and critical areas. He has conducted significant work on neural networks, which are fundamental to modern AI, and adaptive neuro-fuzzy systems, which blend the strengths of neural networks and fuzzy logic. His research also delves into optimisation techniques, a cornerstone for efficient problem-solving, and has even touched upon the challenging domain of earthquake prediction. He is a prominent figure in the advancement of deep learning and computer vision, contributing to breakthroughs in how machines "see" and interpret the world. His focus on Generative AI and Retrieval-Augmented Generation (RAG) highlights his commitment to developing AI that can generate relevant and informed content. Furthermore, his investigations into LLMs and Agentic AI underscore his dedication to pushing the boundaries of intelligent systems. Recognising the growing importance of security in AI, he also explores AI security, alongside the fascinating realm of Quantum Machine Learning (QML). This interdisciplinary approach to research has led to practical applications across various sectors, including the development of sophisticated scientific prediction models, innovations in precision agriculture for optimised farming, the creation of intelligent decision support systems for better insights, and the crucial area of trustworthy autonomous AI systems that operate reliably and ethically.
In his role as an academic mentor, Professor (Dr.) Rana has provided invaluable guidance to numerous students. He has successfully supervised a multitude of PhD's and M.Tech theses and dissertations, helping research scholars and postgraduate students achieve their research goals. His mentorship also extends to supervising both postgraduate and undergraduate projects, fostering the next generation of AI professionals. Beyond formal supervision, he actively contributes to academic mentoring and the broader research development landscape, nurturing talent and encouraging scientific inquiry. His expertise is highly sought after, as evidenced by his frequent invitations to deliver keynote addresses and expert talks. These engagements, focusing on AI, ML, Generative AI, and Data Science, have taken place at both national workshops and prestigious international conferences, allowing him to share his profound knowledge and insights with a wide audience of peers and enthusiasts.
Professor (Dr.) Rana's unwavering commitment lies in the continuous advancement of AI research and education. He is particularly dedicated to fostering innovative, secure, adaptive, and responsible AI practices. His vision is not merely theoretical; he is deeply focused on translating emerging AI technologies into tangible and practical interdisciplinary applications that can address real-world challenges and benefit society. This dedication to practical implementation ensures that his research and teaching have a meaningful and lasting impact on the field of artificial intelligence and its broader applications.
Employment
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Shoolini University Professor2025 - Present
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (48)
- An Adaptive Hybrid Deep–Statistical Framework for Robust Anomaly Detection in Sequential Systems Save
- Trust-Aware Cross-Modal Inconsistency Fusion for Text-Image Fake News Detection in Social Media Save
- Manuscript supporting materials for "Do Specific Cancer Types Show Higher Expression of Depression and Anxiety Linked Genes? A Transparent Computational Study" Save
- Manuscript supporting materials for "Do Specific Cancer Types Show Higher Expression of Depression and Anxiety Linked Genes? A Transparent Computational Study" Save
- Hybrid Transformer-Statistical Framework for Explainable Anomaly Detection in Time-Series Data: Enhancing Operational Monitoring and Security Save
- A Hybrid Statistical–Transformer Autoencoder Framework for Point and Contextual Anomaly Detection in Multivariate Operational Time-Series Data Save
- A Domain-Specific Large Language Model for Security and Non-Security Vulnerability Detection and Remediation in Learning Management Systems Save
- YOLO-based deep learning framework for real-time multi-class plant health monitoring in precision agriculture Save
- A Hybrid Deep Learning and Anomaly Detection Model for Interpretable Analysis of Dental OPG Save
- Adaptive Scale Networks for Language Models: An Empirical Study of Self-adaptive LLMs and Reinforcement Learning Save