"Building the future of edge AI - one face at a time"

class LokeshPatra:
    def __init__(self):
        self.role = "Research Scholar + Associate DSML Developer"
        self.focus = ["Edge AI", "Computer Vision", "IoT Systems", "Ethical AI"]
        self.current_research = "Low-power facial recognition for resource-constrained environments"
        self.education = "Data Science & Machine Learning"
        
    def current_work(self):
        return {
            "project": "BioSentinel Edge",
            "technologies": ["ESP32", "FastAPI", "React", "TensorFlow Lite", "GANs"],
            "deadline": "December 3, 2025",
            "innovation": "Synthetic GAN datasets for bias-free AI training"
        }
    
    def skills(self):
        return {
            "languages": ["Python", "C++", "JavaScript", "SQL"],
            "ml_frameworks": ["TensorFlow", "PyTorch", "scikit-learn", "OpenCV"],
            "backend": ["FastAPI", "Flask", "Node.js", "SQLAlchemy"],
            "frontend": ["React", "TypeScript", "TailwindCSS"],
            "iot": ["ESP32", "Arduino", "MQTT", "I2C/SPI protocols"],
            "cloud": ["AWS", "Azure", "Docker", "PostgreSQL"],
            "tools": ["Git", "PlatformIO", "Jupyter", "VS Code"]
        }

• Phase 4: Building React TypeScript dashboard for real-time face detection monitoring
• Phase 5: Integrating 100k+ synthetic faces from StyleGAN2 + FFHQ datasets
• Phase 6: Quantizing MobileFaceNet (FP32 → INT8) for ESP32 deployment
• Phase 7: Implementing Faiss vector similarity search for 1000+ identities

• Generative AI: StyleGAN2, LCA-GAN for synthetic face generation
• Model Optimization: TensorFlow Lite quantization, pruning, knowledge distillation
• Edge Computing: On-device inference with <2W power budget
• Ethical AI: Bias mitigation, fairness metrics, GDPR compliance
• System Design: Hybrid edge-cloud architectures for IoT

• 3/7 Phases Complete - BioSentinel Edge on track for Dec 3 deadline
• Real-time Face Recognition - 90% accuracy at 20 FPS on ESP32
• Ethical AI Pioneer - 100% synthetic dataset (zero real biometric data)
• Low-Power Design - 1.5W average consumption (battery-operable)
• End-to-End System - ESP32 → MQTT → FastAPI → Database verified
• Production Backend - 120+ identities, 320+ events logged in real-time

Core Research Domains

Edge AI Low-power inference TensorFlow Lite Model quantization ESP32 deployment

Computer Vision Face detection Biometric analysis Multi-face tracking Real-time processing

Ethical AI Synthetic datasets GAN generation Bias mitigation Privacy-first design

IoT Systems Sensor integration MQTT protocols Hybrid architectures Resource optimization

🔬 Active Research Topics

+ Generative AI (StyleGAN2, LCA-GAN) for privacy-preserving training
+ Edge Computing: Deep learning on microcontrollers (<1MB RAM)
+ Ethical Facial Recognition with 100% synthetic data
+ Hybrid Edge-Cloud Architectures for IoT scalability
+ Model Optimization: Quantization, Pruning, Knowledge Distillation

Hybrid edge-cloud facial recognition for resource-constrained environments

Tech: ESP32, FastAPI, React, TensorFlow Lite, Faiss, StyleGAN2
Status: Phase 3/7 Complete | Deadline: Dec 3, 2025

Highlights:

• <500ms end-to-end latency
• <2W power consumption
• 100% synthetic training data
• Multi-modal biometrics (face + HR + GSR)

• I'm working on a 6-week research sprint to build a complete facial recognition system

• I use 100% synthetic data because I believe in ethical AI

• I optimize models to run on devices with <1MB RAM

• My current project runs on <2 watts of power

• I'm building technology for disaster response and remote environments

• "I smile when I blink!" 😊

• Complete BioSentinel Edge (7 phases)
• Publish research paper on edge-based facial recognition
• Deploy production system for real-world testing
• Contribute to open-source computer vision projects
• Write technical blog series on ethical AI + edge computing

⭐ From lightxLK - Building the future of edge AI, one commit at a time!