Edge AI Systems Architect | Decentralized Intelligence Research

I design and prototype computationally efficient AI architectures for deployment on resource-constrained edge systems. My work focuses on bridging high-complexity machine learning models with low-latency, energy-aware execution environments in healthcare, agriculture, and environmental monitoring.

This portfolio documents research prototypes and architectural explorations aligned with long-term Edge-AI system development.

• Decentralized anomaly detection for real-time medical monitoring
• Graph Neural Network (GNN) optimization for biomedical modeling
• Multi-modal AI systems (vision + telemetry + geospatial data)
• Energy-aware and solar-powered edge computation
• Secure and privacy-conscious distributed architectures

A decentralized statistical anomaly detection framework designed for real-time vital signal analysis on single-board computers (SBCs).
Focus areas include signal normalization stability, low-latency inference, and fault-tolerant edge execution.

Research-driven implementation of normalized graph convolution techniques for Protein-Protein Interaction (PPI) analysis.
Emphasis on numerical stability, sparse graph handling, and feasibility of GNN inference under constrained hardware conditions.

A plugin-based experimentation framework integrating computer vision (YOLO-based detection), IoT telemetry, and GPS-linked historical climate data.
Designed to evaluate distributed inference strategies in low-connectivity agricultural environments.

A solar-aware edge architecture prototype combining physico-chemical sensor fusion with AI-based biological indicator detection for ecological monitoring research.

Machine Learning & Edge Deployment

• PyTorch
• TensorFlow Lite
• Graph Neural Networks
• Model Quantization & Optimization
• NVIDIA Jetson & SBC-class hardware

Systems Architecture

• PHP (Senior)
• Laravel
• PostgreSQL / MySQL
• Redis
• RESTful & GraphQL APIs

Distributed Infrastructure

• Docker
• Kubernetes
• Linux Systems Administration
• MQTT
• Event-driven microservices

Biomedical & Data Systems

• Protein interaction network analysis
• Clinical dataset preprocessing (e.g., TCGA)
• Healthcare interoperability concepts (FHIR)
• Privacy-aware system design

• MBBS Coursework Completed – Government Medical College, Thiruvananthapuram
• Diploma in Business Administration (In Progress)

My work integrates medical domain knowledge with systems engineering to explore scalable decentralized AI architectures.

• Stability-aware GNN compression techniques
• Energy-efficient inference scheduling
• Distributed anomaly detection across edge nodes
• Hardware-conscious model design for real-time systems

Singapore-based.
Open to academic collaboration, technical discussions, and systems-level architecture exchange.