
Mr. Arjun Singh Gangwar
Lecturer, School of Computing and Data Science
Arjun is a Lecturer in the School of Computing and Data Science at Sai University, Chennai. With a strong academic foundation in mathematics and data science, he brings a multidisciplinary approach to teaching and research in artificial intelligence and machine learning. He holds an M.Tech in Data Science from the Indian Institute of Technology (IIT) Palakkad (2025) and an M.Sc. in Mathematics from Mahatma Jyotiba Phule Rohilkhand University, Bareilly (2018).
He has qualified multiple prestigious national-level examinations, including CSIR-NET, GATE, and IIT-JAM, and has completed advanced certifications from IITs, Google, IBM, and Stanford University through platforms like NPTEL and Coursera.
He has over five years of teaching experience, having previously served as an Assistant Professor at Future University Bareilly and Utkarsh School of Management & Technology, Bareilly. During his M.Tech, he served as a Teaching Assistant at IIT Palakkad, and also he is the founder of Epsilon Academy, a non-profit YouTube channel aimed at democratizing quality education in STEM.
Mr. Gangwar’s academic and research interests include linear algebra, machine learning, deep learning, time series analysis, graph neural networks (GNNs), physics-informed neural networks
(PINNs), and generative AI. His postgraduate research focuses on soil moisture prediction using explainable AI/ML techniques, incorporating clustering, hybrid deep learning models, and GNN-based architectures.
He has successfully executed several applied AI projects, including:AI-based quiz generation systems,Text summarization using BART and GPT-2,Retrieval-Augmented Generation (RAG) models for media intelligence chatbots.
In addition to his research, he is skilled in developing data-centric applications using tools such as Python, FastAPI, Streamlit, and a wide range of data science libraries. A passionate educator and researcher, Mr. Gangwar is committed to advancing explainable, interpretable, and responsible AI to ensure transparency, fairness, and accessibility in real-world applications.