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Engineered Systems & Case Studies

Engineering systems and empirical research spanning edge microcontrollers, econometric modeling, and structured retrieval pipelines.

Receiving the National Winner recognition on stage from Arduino CEO Fabio Violante for SafeGuard-AI.

National Winner · Physical AI: AI for All Challenge · Qualcomm × ArduinoJan 2026 – Feb 2026

SafeGuard-AI: TinyML Fall Detection System

Real-time on-device fall detection deployed on Arduino UNO Q with quantized inference

Developed SafeGuard-AI, a TinyML fall-detection system designed for real-time, on-device inference. The project was later selected for live demonstration at the Qualcomm booth during the India AI Impact Summit 2026 in New Delhi.

TinyMLTensorFlow LiteQuantizationArduino UNO QIMU SensorsMotion Feature EngineeringOn-Device Inference

Read Full Case Study Arduino Project Hub GitHub Code

Research team at the 3rd International Conference on Infrastructure Development & Sustainability (ICIDS 2025)

First-Author Paper — ICIDS 2025Jul 2025 – Dec 2025

Analyzing Indian Stock Markets Through Correlations: Comparative Insights

First-author empirical research modeling systemic risk and lead–lag dynamics across Indian market sectors

Comparative analysis evaluating dependency measures across Indian equity sectors using empirical time-series correlation matrices.

Pearson CorrelationSpearman RankKendall's TauTime-Lagged Cross-CorrelationMinimum Spanning TreesSystemic RiskTime-Series Preprocessing

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SYSTEM ARCHITECTURE

High-Level System Flow

Academic Content→Retrieval→Context LLM

Academic CollaborationJun 2026 – Present

Moodle AI Assistant

RAG-Based University AI Platform

Developing a Retrieval-Augmented Generation (RAG) based assistant for university academic content, enabling students to interact with course material through context-aware AI responses as part of a broader academic AI initiative.

RAGLLMsAcademic RetrievalContext-Aware QAOpen-Source ModelsModel Comparison

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SYSTEM ARCHITECTURE

Multi-Layered Anomaly Engine

Rules Filter→LLM Signals→Audit Review

Anomaly Detection & MLApr 2024 – Jul 2024

Hybrid Fraud Detection System

Multi-layered anomaly detection combining deterministic heuristic rules and LLM-based signals

A financial anomaly detection pipeline integrating rule-based transaction validation with LLM reasoning signals to flag suspicious financial transactions, suppress false positives, and route ambiguous cases to a human-in-the-loop review workflow.

Hybrid PipelineHeuristic RulesLLM SignalsAnomaly DetectionHuman-in-the-LoopFinancial Risk

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