Distributed Data Pipeline for ETL Processes
Technologies: Python, Ray, Py4web, Docker, AWS
Summary: Developed a scalable ETL pipeline for efficient data extraction, transformation, and loading (ETL) from SQL databases, REST APIs, and CSV files. The pipeline leverages Ray for distributed processing, ensuring high efficiency and scalability in data workflows.
Key Contributions:
- Created a distributed data pipeline architecture using Ray to parallelize ETL tasks.
- Automated data extraction and transformation processes, reducing manual intervention.
- Improved overall ETL processing speed and efficiency through scalable distributed systems.
Browser for Bird Habitat Modeling and Connectivity
Technologies: Py4web, Python, MySQL, Docker, Google Cloud Summary: A web application designed to model and analyze bird habitats using machine learning and parallel computation. The app allows researchers to visualize habitat dynamics and predict the impact of environmental changes on bird populations.
Key Contributions:
- Developed a web interface for visualizing bird habitats and model predictions.
- Implemented parallel processing techniques to handle large-scale computations.
Actor Based MapReduce Framework
Technologies: Distributed Systems, Elixir, Python
Summary: Developed an actor-based MapReduce framework with asynchronous messaging for system isolation, efficient task distribution, and fault tolerance. The framework automatically reassigns tasks in case of node failures, ensuring robustness in distributed environments.
Key Contributions:
- Designed and implemented an actor-based framework to parallelize MapReduce tasks with automatic fault tolerance.
- Leveraged asynchronous messaging for improved system performance and task distribution.
- Achieved significant improvements in system reliability and efficiency through automated task reassignment.
Community Management System
Technologies: React, Java, Spring, AWS
Summary: Built a full-featured community management system that provides user registration, login, CRUD operations, and email notifications. The system is designed to enhance user engagement by providing a secure and user-friendly platform.
Key Contributions:
- Implemented secure user registration and login features using Java and Spring.
- Integrated email notifications to keep users informed of important updates.
- Deployed the system on AWS, ensuring scalability and availability.
Segmentation Using Multitasking Deep Neural Networks
Technologies: PyTorch, Python, OpenCV
Summary: Developed a 3D encoder-decoder network for abdominal organ segmentation using multi-task learning. The model was trained on the BTCV Abdomen dataset, incorporating boundary prediction to enhance segmentation accuracy.
Key Contributions:
- Built a multi-task learning model to simultaneously segment organs and predict boundaries, improving segmentation performance.
- Optimized the deep neural network architecture for 3D medical image segmentation using PyTorch.
- Demonstrated a significant increase in segmentation accuracy compared to traditional single-task approaches.