Hello! I'm Mike Phillips, a data scientist and AI researcher at Vanderbilt University's MASI Lab. My work primarily revolves around leveraging advanced data analysis techniques and AI algorithms to drive innovation in digital imaging and machine learning. This repository contains a collection of my scripts, each designed to address unique challenges in the field of machine learning, simulation, and AI research.
- Expertise: Specialized in statistical modeling, machine learning, and digital imaging.
- Background: My journey in data science blends rigorous academic research with practical applications, particularly in medical image processing and AI model development.
- Vision: Passionate about creating intelligent solutions that leverage data for impactful research and real-world applications.
- Purpose: Processes MRI T1 scans using SLANT-generated segmentation results, creating overlays on original T1 slices. It generates datasets in the Hugging Face format for AI model training.
- Relevance: Demonstrates complex data manipulation, dataset preparation for AI training, and innovative applications in medical imaging.
XNAT Assessor Deletion Tool (Python)
- Purpose: Provides automated functionalities for deleting assessor objects from XNAT sessions, tailored for digital imaging research.
- Relevance: Highlights skills in automation, scripting for data management, and efficient handling of large-scale medical imaging data.
Monte Carlo Psychrotolerant Sporeformer Simulation (R language)
- Purpose: Simulates bacterial growth in milk using Monte Carlo methods and various statistical growth models, aiming to improve estimates of dairy spoilage.
- Relevance: Demonstrates capabilities in complex statistical modeling, probabilistic simulation, and data-driven problem-solving, applicable in diverse data science domains.
In my journey as a data scientist, I've always been fascinated by the power of visualization in storytelling. My recent endeavors have been focused on dynamic visualization and interactive dashboards. These platforms empower users to customize their data views while maintaining the integrity of the overarching narrative. This approach not only enhances user engagement but also ensures that key insights are effectively communicated.
While I have a wealth of experience in static data visualization, my recent projects have been exploratory ventures into dynamic visualization using D3 and React in JavaScript. These projects are part of my learning journey and may not reflect the professional standards of my other work, but they showcase my growing interest and capabilities in this exciting area of data science.
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Life Expectancy Visualization
- An interactive exploration of life expectancy data.
- View Visualization
- View Code on GitHub
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Best Rapper Debate
- A fun, interactive way to engage in the never-ending debate of who is the best rapper.
- View Visualization
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Journey Through Middle Earth
- A dynamic visualization that takes you on a journey through Middle Earth.
- View Visualization
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Global Migration Patterns
- An interactive dashboard depicting global migration patterns.
- View Visualization
My approach to dynamic visualization centers on user interaction and customization, ensuring that data exploration remains intuitive and informative. These projects reflect my commitment to continuous learning and my enthusiasm for harnessing the latest technologies in data visualization.
I am always open to collaborative opportunities and discussions about potential applications of these scripts or new challenges in the field of data science and AI. Feel free to reach out to me for any queries, suggestions, or collaboration ideas.
Thank you for visiting my repository. I hope you find these scripts insightful and indicative of my passion and capabilities in data science and AI research.