My Projects
VIVA MUN Leadership
Largest MUN in Andhra Pradesh
Led 19-member team organizing largest MUN in Andhra Pradesh with 500+ delegates. Secured nearly $6000 in sponsorship funding, built conference website, implemented SEO techniques, and developed automation scripts for operations.
AgroLend Platform
AI-Powered Financial Literacy for Farmers
Developed an AI chatbot that speaks to South Indian farmers in their native languages, helping them understand financial schemes, loans, and insurance. Features AI-gen video content for easy comprehension with content verified by State Bank of India.
Academic Excellence
Stanford ML Specialization + Advanced Placement
Completed Stanford University’s Machine Learning Specialization with 99.92% final grade. Covered supervised learning, unsupervised learning, reinforcement learning, and neural networks. Gained expertise in TensorFlow, Keras, and scikit-learn libraries.
Purpose Academy
UC Berkeley Entrepreneurship Program
Selected among 25 students from India for prestigious entrepreneurship program at UC Berkeley’s Sutardja Center for Entrepreneurship & Technology. Participated in
week-long immersion learning Berkeley Method of Entrepreneurship, visited tech companies
(Intel, Uber, NVIDIA).
Code-Mixed NLP (Awarded CREST Gold)
English-Telugu Text Processing
Designed Natural Language Processing systems for English-Telugu code-mixed text processing utilizing Perceptron Classifiers, Hidden Markov Models, and Recurrent Neural Networks. Focused on making AI language-inclusive for diverse communities.
Crop Disease Detection
Ensemble AI Models for Mobile Agriculture
Built ensemble models combining VGG16, InceptionV3, MobileNetV2, and custom CNNs to detect crop diseases. Trained on 60,000+ plant images covering 40+ disease
types, achieving ~99% accuracy with mobile optimization for smallholder farmers. (Accepted to IEEE ICIH 2025)
Crop Health Assessment
AI-Powered Pest & Deficiency Detection
Developed dual-function computer vision system combining nutrient deficiency detection and pest identification using CNNs. Achieved over 90% accuracy in detecting nitrogen, phosphorus, potassium deficiencies. Created mobile-optimized deployment for smallholder farmers to reduce fertilizer costs.