About

About DecodedPapers

Welcome to DecodedPapers — where I share insights on Artificial Intelligence and Machine Learning papers and books. As an artificial intelligence and machine learning practitioner, I break down complex AI and ML papers into digestible practical implementations, showing you not just the theory but how to bring these ideas to life with code.

Here, I hope you’ll find:

• Step-by-step reproductions of state-of-the-art papers.
• Real-world adaptations that demonstrate how theoretical concepts can solve practical problems.
• Deep dives into implementation details that papers often gloss over.

Whether you’re a researcher, an engineer, or a beginner in data science trying to apply the latest techniques, or just curious about the bleeding edge of machine learning, DecodedPapers bridges the gap between academic research and practical application. All code is available on GitHub, allowing you to experiment, extend, and build upon these implementations for your own projects.

About Me

Hi, my name is Mayer

I am a Data Scientist at the Centers for Disease Control and Prevention (CDC), where I help teams across the agency build and deploy AI/ML solutions that modernize public health data systems.

My mission is to empower public health professionals to leverage data science, machine learning, and AI responsibly and effectively, from NLP and computer vision to agentic AI and production MLOps.

Before joining the CDC Data Science Upskilling Program, I spent over 15 years in information systems development and health informatics, specializing in patient identity management, biometric systems, and probabilistic record matching. I developed algorithms for deduplicating HIV case surveillance and EMR patient-level data, and created interactive tools for comparing data matching approaches that have been deployed in production public health surveillance settings.

At CDC, I’ve been instrumental in advancing AI/ML capabilities across the agency through technical consultation, hands-on problem-solving, and creating learning materials that bridge the gap between research and real-world application. I work on projects spanning Big Data analytics, deep learning, NLP, computer vision, and data visualization.

I actively share my knowledge through research collaborations, open-source contributions, and technical documentation. I believe in making advanced AI accessible and practical for real-world public health applications.

Below is a list of technologies (mostly open-source frameworks, libraries, and languages) I regularly use and enjoy working with. If you want to see more of what I do or have done, check out my GitHub.

🤖 Machine Learning

Transformers, PyTorch, TensorFlow, Scikit-Learn, Hugging Face, BERTopic, Langchain, OpenAI API and Agents SDK, Weights & Biases.

☁️ Cloud & MLOps

Azure ML Services, Amazon SageMaker, Databricks, Docker, GitHub Actions.

🏗️ Data & Development

Python, JavaScript/TypeScript, SQL, Pandas, NumPy, FastAPI, React, Next.js, Jupyter.

📫 Connect

LinkedIn · Based in Atlanta, Georgia