Adyaa GB

Bengaluru City, Karnataka, India

@adyaa_gb

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🎓 Bachelor of Technology in Computer Science and Engineering (AI & ML) at PES University · Graduating 2027

About

Computer Science and Engineering student at PES University specializing in AI & ML.

<3 months

SQL RAG HTML Java LLMs React Flask CI/CD Python FastAPI Node.js Deep Learning Multithreading Computer Vision Machine Learning Microservices Architecture Natural Language Processing API Integration Understanding

<3 months

Git Linux MySQL Redis Docker GitHub MongoDB Kubernetes Atlassian JIRA Amazon Web Services AWS

AERIX – UAV-Based Post-Disaster Autonomous Human Detection

Python, YOLOv8-Seg, OpenCV, TensorFlow

Developed an AI-powered UAV disaster response system using YOLOv8 segmentation to detect disaster-affected regions and improve autonomous human detection. Built a preprocessing and augmentation pipeline with annotation validation and two-stage model training, achieving 71.2% mIoU.
Forensic Evidence Management System

Python (Flask), MySQL, SQL, Bootstrap, HTML

Developed a secure web-based forensic evidence management system with role-based authentication, password hashing, and controlled access for secure evidence management. Designed a MySQL relational database using stored procedures, triggers, and analytical SQL queries to ensure data integrity, traceability, auditability, and efficient retrieval of forensic records.
Distributed Consensus Engine – Raft Protocol

Java, Spring Boot, Docker, Distributed Systems, Raft Consensus Algorithm

Developed a distributed consensus engine implementing the Raft algorithm with leader election, log replication, heartbeat management, and crash recovery to ensure fault-tolerant distributed coordination. Built a collaborative distributed application on top of the consensus engine, ensuring automatic leader failover, consistent state replication, and fault-tolerant coordination across multiple nodes.

ValveX – Smart Water Intelligence System · 2026

1st Place, EmbedX 2.0 2026. Designed an ESP32-based IoT system integrating MQTT, Node-RED, continuous flow anomaly detection, and real-time monitoring for leak detection and automated valve/pump control. Implemented predictive water-usage analytics and remote monitoring for real-time data-driven decision making.
Smart Adaptive Machinery · 2025

HCLTech Ideathon 2025. Selected among the Top 7 finalist teams for proposing a Neuromorphic Edge AI solution for real-time adaptive mechanical automation using Spiking Neural Networks (SNNs) and event-driven edge computing.