Shubham Kumar Dubey

Shubham Kumar Dubey is an Assistant Professor at the School of Computer Science, UPES Dehradun. He is currently pursuing a Ph.D. at Indian Institute of Technology Hyderabad, where his research focuses on computer vision, deep learning, and autonomous systems. His doctoral research is conducted in collaboration with Research Centre Imarat (RCI), DRDO, emphasizing advanced object detection methods and false positive elimination in video analytics.

He completed his M.Tech from Indian Institute of Technology Hyderabad and has published research in reputed international conferences including VISAPP and ICMV. His work explores improving the reliability of deep learning-based detection models using filtering, generative modeling, and video-sequence-based consistency approaches.

Before joining academia, Shubham worked in the EdTech industry at BYJU'S in a marketing role, gaining valuable experience in strategy, communication, and technology-enabled learning solutions. His transition to research and teaching reflects a strong commitment to advancing AI-driven innovation and engineering education.

Computer Vision | Deep Learning | Autonomous Vehicles | Object Detection | Video Analytics | False Positive Reduction | AI for Automation

Shubham believes in simplifying complex concepts through interactive, example-driven, and story-based learning. He uses frequent feedback loops to understand student challenges and adapts his teaching style to promote clarity and real-world understanding.

His classroom approach emphasizes conceptual clarity, hands-on exposure, and a smooth, simplified learning experience over pure theoretical instruction.

Machine Learning | Deep Learning | Computer Vision | Data Structures | Algorithms | C Programming | Python Programming

Qualified GATE examination. Published two papers in international conferences including VISAPP and ICMV during Ph.D. research. Conducted doctoral research in collaboration with Research Centre Imarat (RCI), DRDO.

Eliminating False Positive Elimination in Object Detection Methods for Videos — Presented at ICMV. This work focuses on improving object detection robustness by reducing false positives in video sequences.

Enhancing Object Detection Accuracy with Variational Autoencoders as a Filter in YOLO — Presented at VISAPP. This research introduces a VAE-based filtering mechanism to enhance YOLO performance on complex scenes.

Shubham actively contributes to research in computer vision and deep learning with a focus on building reliable detection systems. He participates in collaborative research activities involving autonomous systems, video intelligence, and AI-driven optimization.

His ongoing work integrates practical industry requirements with academic research to develop systems suitable for real-world deployment.

Email: subhrasankar.chatterjee@ddn.upes.ac.in