Medical Imaging AI
Developing robust and interpretable methods for chest X-ray analysis, disease localization, segmentation, and uncertainty-aware clinical AI.
Kiran Shahi
AI Researcher | Computer Vision Engineer | Applied AI for Medical Imaging and Research Software
I build research-driven AI systems at the intersection of computer vision, medical imaging, explainable AI, and engineering software. My work combines scientific research with real-world product development, with a focus on trustworthy AI, visual intelligence, and deployable tools for healthcare applications.
Welcome
Welcome to my academic website. I am a researcher and software engineer interested in developing practical and scientifically grounded AI systems. My work spans medical image analysis, explainable deep learning, video and visual understanding, and applied AI tools for engineering applications.
I am particularly interested in research that bridges theory and deployment: building systems that are not only accurate, but also interpretable, useful, and scalable in real-world environments.
Featured Research Areas
Computer Vision
Exploring weak supervision, visual explanations, temporal modeling, and next-generation vision systems for challenging real-world problems.
Explainable AI
Studying how model decisions can be interpreted and trusted, especially in high-stakes domains such as healthcare.
Research Software Systems
Designing practical AI software systems that connect experiments, model outputs, and deployable workflows.
Featured Projects Preview
- Weakly Supervised Pneumonia Localization from Chest X-Rays
- Uncertainty-Aware Lung Segmentation
- Research-Driven AI Software Systems
- Temporal Video Matting Research
Quick Bio
Kiran Shahi is a researcher and software engineer working in artificial intelligence, computer vision, medical imaging, explainable AI, and research software systems. His work focuses on building trustworthy and deployable AI systems that connect scientific research with practical impact.
Closing Statement
I am always interested in research collaborations, interdisciplinary projects, and opportunities that combine scientific rigor, software engineering, and real-world impact.