Twitch+: Personalized Recommendation Engine
- Technologies: Spring Boot, React, MySQL, AWS RDS, OpenFeign
- Description: Full-stack application for personalized Twitch resource recommendations
- Key Achievements:
- Enhanced user engagement by 25% through personalized content recommendations
- Implemented RESTful APIs to fetch real-time Twitch resources
- Hosted MySQL database on AWS RDS with efficient CRUD operations
- Developed content-based recommendation algorithm
- Containerized and deployed to AWS App Runner for scalable service
Email Helper: AI-Powered Email Generation
- Technologies: Python Flask, OpenAI GPT-3.5, Langchain, AWS DynamoDB, AWS Lambda
- Description: Conversational web app for generating email scripts using AI
- Key Achievements:
- Integrated OpenAI's GPT-3.5 Turbo and Langchain for sophisticated AI-driven communication
- Hosted NoSQL DynamoDB for email history management
- Performed ETL tasks on Databricks
- Containerized application on AWS ECR and deployed to AWS App Runner
- Automated testing, containerization, and deployment using GitHub CI/CD
Plant Forager Mobile App
- Technologies: React Native, Expo, Node.js, Express.js
- Description: Cross-platform mobile application for plant discovery and personal plant repository
- Key Features:
- Designed nature-themed UI using Figma
- Implemented RESTful APIs for user profiles and plant collections
- Utilized native mobile functionalities:
- Location services for plant locating
- Camera for plant profiling
- Calendar and push notifications for plant ripening alerts
Incentivized Truthful Communication for Federated Bandits
- Publication: Co-author, ICLR 2024 Journal
- Research Focus: Federated Learning and Incentive Mechanisms
- Key Contributions:
- Developed an innovative incentive-compatible communication protocol for federated bandit learning
- Theoretically proved the model's sub-linear regret and communication cost
- Designed and conducted comprehensive numerical experiments in multi-client federated bandit settings
- Challenged existing assumptions about client truthfulness in distributed learning systems
Graduate Program Recommendation System
- Technologies: TensorFlow, Scikit-learn, Word2Vec
- Project Scope: Machine Learningļ¼ Recommendation System
- Technical Achievements:
- Engineered a sophisticated recommendation system using application attribute analysis
- Preprocessed and cleaned a large dataset of 80,000 subject records using advanced data pipelines
- Implemented advanced feature representation techniques:
- Applied Word2Vec for converting categorical variables into dense vector representations
- Significantly enhanced model's information extraction capabilities
- Conducted comprehensive algorithm evaluation:
- Compared multiple machine learning approaches including K-Nearest Neighbors and ResNet
- Achieved 73% accuracy through meticulous hyperparameter tuning
- Demonstrated expertise in model selection and optimization