Staff AI Engineer | Senior Data Scientist | AI Mentor | Author

I'm passionate about Artificial Intelligence, Machine Learning, Genetic Algorithms, and empowering developers with knowledge.
As the author of "Hands-On Genetic Algorithms with Python" (Second Edition), I specialize in developing scalable AI solutions and have a strong background in Python as well as Java development.

• 🚀 Current Role: Staff AI Engineer at AIngelz, designing and implementing agentic AI applications in the healthcare space, with a focus on explainability, safety, and real-world decision support.
• 📘 My Book on Genetic Algorithms
• 🎓 Adjunct Professor at Jacksonville University, teaching AI
• 🌟 Founder of AI 4 Java
• 🧠 Community leader for the:
  • Jacksoville Google Developer Group
  • AI for Enterprise Virtual User Group
  • Jacksonville Java User Group
• ⚙️ Patent Holder: UAV with ducted rotors

I enjoy sharing my knowledge and engaging with the global developer and AI community. Here are some of my recent and upcoming talks:

• Jacksonville Python User Group (PyJax), June 2025
  Talk: "A Journey Through NLP and Genetic Algorithms"
  Delving into how NLP and genetic algorithms can be used to tackle complex optimization and language-based problems.

• Global Data AI Virtual Tech Conference, January 2025
  Talk: "Applying Genetic Algorithms to Real-World Machine Learning and Artificial Intelligence Problems"
  Discussed how genetic algorithms can optimize machine learning workflows and solve real-world AI challenges.

• GDG Pescara DevFest (Italy), October 2024
  Talk: "Unlocking the Secrets of the Mystery-Word Game: A Journey Through NLP and Genetic Algorithms"
  Explored the application of genetic algorithms and NLP techniques in solving complex word-based puzzles.

I look forward to continuing these conversations at future events!

I'm the author of "Hands-On Genetic Algorithms with Python", a comprehensive guide to applying genetic algorithms to real-world AI and machine learning problems.

• 📖 Official GitHub Repository for the Book
• 🎥 Watch the Tutorial Playlist
• 🛒 Get the Book on Amazon
• 🎁 Download a Free Chapter

• How to build, visualize, and optimize genetic algorithms.
• Applying genetic algorithms to search, optimization, and AI-related tasks.
• Real-world Python implementations that improve machine learning models.

You can find full working examples of the following:

• Using the DEAP Framework
• Combinatorial Optimization
• Constraint Satisfaction
• Optimizing Continuous Functions
• Enhancing Machine Learning Models Using Feature Selection
• Hyperparameter Tuning of Machine Learning Models
• Architecture Optimization of Deep Learning Networks
• Reinforcement Learning with Genetic Algorithms
• Natural Language Processing
• Explainable AI, Causality and Counterfactuals with Genetic Algorithms
• Accelerating Genetic Algorithms: The Power of Concurrency
• Beyond Local Resources: Scaling Genetic Algorithms in the Cloud
• Evolutionary Image Reconstruction with Genetic Algorithms
• Other Evolutionary and Bio-Inspired Computation Techniques

Feel free to open issues and share your thoughts!