• Evaluated parameterized quantum circuits with CUDA-Q and PyTorch for quantum ML scalability analysis.
• Reviewed quantum-optimized ML methods in noisy data for robust 3D shape fitting and visual reconstruction

• Built production-grade anomaly detection pipelines with 100% recall and >95% precision for quality assurance.
• Developed a defect detection pipeline using foundational models, reducing manual compliance audit time by 70%.
• Implemented a VLM-based text recognition pipeline with 85% accuracy on complex backgrounds to automate previously manual audits.

• Simulated quantum ML models for pattern retrieval to establish the feasibility of hybrid AI–quantum methods.
• Applied contrastive learning to enhance the accuracy of retrieved queries by 52% using PyTorch.
• Converted large vision datasets into binary representations using autoencoder to reduce storage overhead by 35%.

Zero-shot Retail Object Recognition

• Developed an object recognition pipeline with >80% accuracy for inventory management using TensorFlow.
• Applied unsupervised learning for real-time object segmentation of retail shelf images, reaching >90% accuracy.
• Applied embedding similarity from pre-trained models to improve object recognition on unlabeled datasets.

AI-assisted Retinal Disease Prognosis

• Implemented a TensorFlow-based classification model for disease severity grading, achieving AUC of 0.98.
• Integrated SHAP for interpretability, quantifying parameter influence on model predictions.
• Curated and clinically validated datasets for eye disease diagnosis, ensuring high-quality training data.

• Course: AI for Everyone (20IC206T)

Clinical Biomarker Assessments for Retinal Diseases

• Led a team of 5 interns to develop a clinician-controlled image processing pipeline for disease parameter analysis.
• Achieved results within ±8% of research benchmark SIVA in 3 months using OpenCV and TensorFlow.
• Conducted a review of AI-based retinal imaging telecare services in India to enhance clinical outreach.

Context-Sensitive Semantic Search Tool

• Developed a semantic search tool for impact analysis in software testing, achieving >95% accuracy.
• Generated embeddings using a pre-trained language model for semantic mapping of test cases.
• Built an interactive visualization tool for search results using Python, t-SNE, and matplotlib.