Sakhinala Sanjay Bhargav

Challenge: Electromagnetic simulations for 6G antenna design required 500+ CST API calls per design iteration, each taking 15-20 minutes—making optimization prohibitively expensive and time-consuming for production environments.

• Designed hybrid SAC + LightGBM surrogate model architecture with uncertainty quantification
• Implemented custom Gymnasium environment with fabrication-feasible reward shaping
• Developed multi-fidelity training pipeline reducing simulation dependency by 65%

Outcome: Achieved 65% reduction in simulation costs, 40% fewer episodes to convergence, and uncertainty-aware predictions enabling safe real-world deployment. Research published in IEEE proceedings.

Challenge: Most RAG systems only support English, leaving 1.3 billion Indic language speakers without accessible document QA tools. Existing multilingual solutions lacked production-grade OCR and semantic chunking for complex documents.

• Integrated multilingual dense embeddings with cross-encoder reranking for 12+ Indian languages
• Implemented OCR pipeline (PyTesseract) for scanned PDF processing
• Deployed production FastAPI service with semantic chunking for long-context documents

Outcome: Production-ready RAG pipeline supporting 12+ Indian languages with accurate document retrieval and context-aware answers, enabling multilingual accessibility for enterprise knowledge bases.

Challenge: Traditional messaging apps store messages on centralized servers, creating privacy vulnerabilities. Need for truly secure peer-to-peer communication with enterprise-grade encryption without server intermediaries storing plaintext.

• Engineered hybrid AES-256 + RSA-2048 cryptographic system for maximum security
• Implemented zero-knowledge architecture with client-side encryption and secure key exchange
• Built multi-threaded TCP socket server with SHA-256 integrity verification for real-time messaging

Outcome: CLI-based messenger with enterprise-grade end-to-end encryption, zero server knowledge of message content, and multi-user support—ensuring complete privacy for sensitive communications.

Challenge: Satellite imagery classification for land-use monitoring required interpretable models beyond black-box accuracy. Urban planners and environmental agencies needed transparent decision-making for policy guidance.

• Developed CNN architecture trained on EuroSAT dataset (10 land-use classes)
• Integrated Grad-CAM explainability layer for visual feature attribution
• Implemented model interpretability pipeline for transparent predictions

Outcome: Achieved 89% test accuracy across 10 land-use categories with explainable AI visualizations showing which image regions influenced predictions, enabling trustworthy deployment for environmental monitoring.

Challenge: Network administrators lacked comprehensive tools to benchmark DNS resolver performance globally. Existing solutions didn't provide statistical rigor or actionable insights for infrastructure decisions.

• Built automated benchmarking framework testing latency across global DNS resolvers
• Implemented statistical analysis pipeline with percentile metrics and reliability scoring
• Developed CLI tool with automated reporting and network troubleshooting diagnostics

Outcome: Production-grade DNS performance analysis framework enabling network teams to make data-driven resolver selections, troubleshoot outages, and optimize query latency for enterprise infrastructure.