Kashif Mazhar | Computer Science | Research Excellence Award

Mr. Kashif Mazhar | Computer Science | Research Excellence Award

Mr. Kashif Mazhar | Computer Science | Research Scholar at Motilal Nehru National Institute of Technology Allahabad | India

Computer Science professional Mr. Kashif Mazhar is an accomplished Assistant Professor and Doctoral Researcher recognized for his strong academic foundation and impactful research in Artificial Intelligence and Data Science. Mr. Kashif Mazhar currently serves as an Assistant Professor in the School of Computer Science (Data Science Cluster) at the University of Petroleum and Energy Studies (UPES), Dehradun, while pursuing his Ph.D. at Motilal Nehru National Institute of Technology (MNNIT) Allahabad, where his doctoral research focuses on Explainable Artificial Intelligence (XAI) for brain tumor MRI classification and segmentation using advanced deep learning models integrated with LIME, SHAP, and Grad-CAM. Mr. Kashif Mazhar holds an M.Tech and B.Tech from the University of Allahabad and has over five years of combined teaching and research experience, including roles as Teaching Assistant at MNNIT Allahabad, Researcher at IIM Jammu, and Data Science Instructor at Simplilearn. His research interests span Explainable AI, Medical Imaging, Social Network Analysis, and AI-driven financial analytics, supported by strong research skills in Python, data analysis, supervision, and scientific reporting. Mr. Kashif Mazhar has published in high-impact Q1 journals and is UGC-NET qualified and GATE certified, reflecting his academic excellence and competitive merit. In conclusion, Mr. Kashif Mazhar exemplifies a forward-looking academic whose interdisciplinary expertise, teaching leadership, and commitment to trustworthy AI position him as a promising contributor to future advancements in Computer Science.

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Featured Publications

Model-agnostic explainable artificial intelligence methods in finance: a systematic review, recent developments, limitations, challenges and future directions
– Artificial Intelligence Review, 2025
Decoding the black box: LIME-assisted understanding of Convolutional Neural Network (CNN) in classification of social media tweets
– Social Network Analysis and Mining, 2024
A survey on methods for explainability in deep learning models
– International Conference on Machine Intelligence, Tools, and Applications, 2024