ML-Based Quality Control System
Skills & Technologies
Overview
Developed a machine learning-based quality control system for manufacturing defect detection. The system uses convolutional neural networks to automatically identify and classify defects in real-time production environments, significantly reducing manual inspection time and improving accuracy.
Work Done
- Collected and annotated dataset of 50,000+ product images with various defect types
- Designed and implemented CNN architecture optimized for real-time inference
- Developed data augmentation pipeline to improve model robustness
- Created REST API for integration with existing production systems
- Deployed model on edge devices for low-latency processing
- Conducted extensive testing and validation across multiple production lines
Results & Impact
- Achieved 98.5% accuracy in defect detection, surpassing human inspector performance
- Reduced inspection time by 75%, allowing for 100% product inspection
- Decreased false positive rate to less than 2%
- Estimated cost savings of $500K annually from reduced defect-related returns
- Published research paper at IEEE International Conference on Machine Learning
- System deployed across 5 manufacturing facilities
Future Prospects
Planning to extend the system to handle multiple product types simultaneously and integrate predictive maintenance capabilities. Exploring federated learning approaches to train models across multiple facilities while maintaining data privacy. Collaborating with industry partners to create standardized defect classification framework.
E-Commerce Platform
Skills & Technologies
Overview
Built a full-stack e-commerce platform from scratch supporting multiple vendors, real-time inventory management, and secure payment processing. The platform handles thousands of transactions daily and provides comprehensive analytics for vendors.
Work Done
- Architected scalable microservices infrastructure
- Implemented responsive frontend with React and Redux
- Developed secure authentication and authorization system
- Integrated Stripe payment processing with webhook handling
- Built real-time inventory synchronization across warehouses
- Created vendor dashboard with sales analytics
Results & Impact
- Platform processes 10,000+ daily transactions
- Supports 200+ active vendors
- Achieved 99.9% uptime over 12 months
- Mobile-first design with 95% Lighthouse score
- Conversion rate 25% higher than previous platform
Future Prospects
Implementing AI-powered recommendation engine and expanding to mobile apps for iOS and Android. Planning to add live chat support and augmented reality product preview features.
Supply Chain Optimization Using OR
Skills & Technologies
Overview
Research project focused on optimizing multi-echelon supply chain networks using mixed-integer linear programming. Developed mathematical models to minimize total costs while meeting service level requirements and capacity constraints.
Work Done
- Formulated MILP optimization model for network design
- Analyzed historical data from 3 years of operations
- Implemented solution algorithm using Gurobi optimizer
- Conducted sensitivity analysis on key parameters
- Validated results through simulation studies
- Developed visualization tools for decision support
Results & Impact
- Achieved 18% reduction in total supply chain costs
- Improved delivery time by 12% on average
- Reduced inventory holding costs by 22%
- Identified optimal locations for new distribution centers
- Findings presented at INFORMS Annual Meeting
Future Prospects
Extending model to incorporate uncertainty and risk management. Exploring integration with machine learning for demand forecasting to create adaptive optimization framework.
Real-Time Analytics Dashboard
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Budget Tracker App
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Autonomous Robot Navigation
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Manufacturing Process Analysis
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Microservices Migration
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Weather Visualization Dashboard
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Real-Time Chat Application
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Energy Consumption Prediction Model
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Recipe Sharing Platform
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Distributed Deep Learning Framework
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Cloud-Based Inventory Management
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IoT Smart Home Controller
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