I am a dedicated research enthusiast with a strong passion for lifelong learning, teaching, and technological innovation. With a unique blend of academic insight and real-world industry experience, I bring a holistic approach to both research and education. My professional journey spans roles in the software industry as a software engineer and application engineer, teaching in academia, and conducting focused research in Artificial Intelligence and Machine Learning—particularly in the areas of Computer Vision and Medical Image Analysis.

My research work has led to the successful execution of several AI/ML-based projects and publications in reputed international journals and conferences. As a faculty member and researcher, my mission is to inspire and empower students, drive impactful research, and contribute meaningfully to academic and institutional development.

Success is not about being better than someone else. It’s about being better than you used to be.

• Medical Image Analysis & Cephalometrics
• Developed deep learning and classical computer vision models for automatic cephalometric landmark annotation.
• Investigated ROI-based extraction, extended template matching, and feature combinational study and transfer learning (e.g., YOLOv8, Detectron2) for improving annotation accuracy.
• Conducted comparative analyses of regression vs. classification techniques and ensemble deep learning methods for landmark localization.
• Collaboratively curated a comprehensive cephalometric image dataset in partnership with a medical university to support landmark annotation research.
• Crack Detection & Infrastructure Monitoring
• Built and evaluated advanced U-Net architectures and foundation models like Crack-SAM for pixel-level crack segmentation.
• Explored ensemble techniques and interpretability tools to enhance model transparency in civil infrastructure analysis.
• Gesture Recognition & Sign Language Processing
• Worked on Indian Sign Language (ISL) recognition using feature fusion, contour-matching, CNN-based and transformer-based deep learning approaches.
• Created novel datasets and segmentation techniques for improved hand gesture recognition.
• EEG Signal Classification
• Applied deep learning to motor imagery EEG signal classification, aiding brain-computer interface development.
• Academic Output
• Authored and reviewed peer-reviewed articles across indexed journals and international conferences.
• Research impact spans healthcare, civil engineering, and assistive technology.
• Research Interests
• Medical Image Analysis, Computer Vision, AI/ML

• Awarded MHRD Merit Scholarship during undergraduate studies in Engineering (2008–2012) for consistent academic excellence.

• Qualified GATE (Computer Science) multiple times, demonstrating strong conceptual proficiency (2012, 2018, 2019, 2020).

• Qualified and awarded UGC-NET (Assistant Professorship) in Computer Science, July 2019.

• Awarded UGC-NET Junior Research Fellowship (JRF) in Computer Science, November 2021.

• Conferred Senior Research Fellowship (SRF) by UGC, recognizing continued research excellence, February 2024.