A comprehensive overview of my academic background, research experience, publications, and achievements.
I am an AI researcher working at the intersection of computer vision, medical image analysis, and multimodal learning. My research focuses on developing deep learning systems that can understand complex visual and linguistic information, with a particular interest in vision-language models, representation learning, and the interpretability of large multimodal models. I will be joining the University of Bonn as a Master’s student in Winter Semester 2026, where I aim to deepen my research in these areas and contribute to high-impact work in artificial intelligence.
Research-focused Master’s program in computer science with emphasis on machine learning, computer vision, and artificial intelligence.
Undergraduate engineering degree with a focus on AI, machine learning, and deep learning research.
“Automated Cervical Vertebral Maturation Staging from a Continuous Perspective”
Conducting research on vision-language model robustness, interpretability, and medical imaging report generation within the Computer Vision department.
Collaborated on deep learning research for medical image analysis, focusing on vertebral maturation staging and ordinal learning approaches.
Worked on machine learning and computer vision research under faculty guidance, gaining foundational research experience in deep learning methodologies.
Completed undergraduate research in AI and deep learning, culminating in a thesis on automated cervical vertebral maturation staging.
An automated approach for assessing cervical vertebral maturation stages using deep learning and ordinal learning, enabling continuous-stage prediction from lateral cephalometric radiographs.
An investigation into the failure modes, robustness, and internal mechanisms of vision-language models, with a focus on noise robustness and mechanistic interpretability.
Automated assessment of cervical vertebral maturation stages using deep learning and ordinal learning approaches for clinical decision support in orthodontics.
Panjab University / PGIMER
2024 — 2025
Research investigating the failure modes, robustness, and internal mechanisms of vision-language models, including noise robustness and mechanistic interpretability.
MBZUAI
2025
Multimodal AI systems leveraging vision-language models for medical imaging and automated radiology report generation.
MBZUAI
2025
Research involving multimodal learning for dental imaging tasks, combining visual and structured data for improved diagnostic support.
2025
Selected for an oral presentation at a NeurIPS 2025 Workshop.
ConferenceResearch paper accepted for presentation at IEEE ICCCNT 2025.
Conference