PRATIK PRIYANSHU

A research-grade framework for fair, controlled comparison of classical graph neural networks, variational quantum circuits, and hybrid classical–quantum architectures for molecular property prediction. Rather than assuming quantum advantage, the goal is to isolate architectural effects under consistent data preprocessing, batching, training, and evaluation protocols.

A multi-agent environmental monitoring system that predicts Harmful Algal Blooms (HABs) across German lakes by fusing satellite imagery, water quality sensors, weather data, and visual analysis through autonomous AI agents communicating via Google's Agent-to-Agent (A2A) protocol.

A research-grade, trust-aware Retrieval-Augmented Generation (RAG) system that integrates domain gating, hybrid retrieval, evidence clustering, contradiction detection, and confidence calibration to minimize hallucinations in high-stakes environments.

Built an end-to-end privacy-preserving ML system enabling secure inference on sensitive healthcare data without exposing plaintext inputs to the server. The model never sees raw patient features — only encrypted vectors.

A modular ADAS perception and safety stack integrating camera, LiDAR, and radar fusion with interaction-aware prediction, explainable AI, safety diagnostics, and scenario validation, built to mirror German OEM architecture principles.