Business student with a technology focus, demonstrating programming proficiency and deep understanding of business concepts, applying problem-solving abilities to enhance innovation.
Image Cartoonizer: Developed a Python-based desktop application that converts user-uploaded images into cartoon-style visuals using OpenCV, achieved over 90% accuracy in producing smooth, clear cartoon effects in testing scenarios, applied more than 3 advanced filters including bilateral filtering, edge detection, and adaptive thresholding, and reduced processing time to under 2 seconds per image for real-time performance
Chemico - AI-powered chemical assistant : Developed Chemico, a generative AI-based tool that predicts chemical reactions and visualizes 3D molecular structures, achieved a 90%+ success rate in correctly identifying and mapping input reactions in test scenarios, supports 20+ types of common organic reactions, making it suitable for educational and research use
AI–Quantum Hybrid Systems in Biotechnology: Engineered a Qiskit-based quantum-ML pipeline for DNA sequence modeling and cancer prediction., Achieved 85%+ accuracy in forecasting cancer cell growth using quantum classifiers., Reduced genomic feature complexity by 45%, optimizing model interpretability., Enabled early-stage tumor detection using time-series analysis of DNA sequences.
AI-Quantum Hybrid Systems in Biotechnology, INCEEE2025045, Secured 1st Prize at Institute R&D 2025 for hybrid AI–quantum model with 92% accuracy in DNA prediction., Achieved 40% boost in genomic classification efficiency; presented at INCEEE 2025, NIT Warangal., Reduced feature dimensionality by 45% on genomic time-series data for early disease detection.