Eyal Wirsansky
Senior data scientist, Artificial Intelligence mentor
Jacksonville, Florida, United States
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Eyal Wirsansky is a Staff AI Engineer, a seasoned software developer, and a recognized leader in the AI community. He currently designs and implements agentic AI applications in the healthcare space, with a focus on explainability, safety, and real-world decision support.
Eyal’s graduate research focused on genetic algorithms and neural networks, culminating in a novel supervised learning method that fuses the strengths of both. Over the past 25+ years, he has contributed to breakthroughs in Voice over IP, healthcare systems, developer tooling, and drone technologies.
He is the author of Hands-On Genetic Algorithms with Python (Second Edition), a comprehensive guide to applying evolutionary strategies to modern machine learning problems.
In addition to his professional work, Eyal teaches AI as an adjunct professor at Jacksonville University, leads the Jacksonville Java User Group and the AI for Enterprise Virtual User Group, and writes the AI4Java blog to help developers bridge the gap between software engineering and artificial intelligence.
Area of Expertise
Topics
The Wonderful World of Bio-Inspired Computing
Bio-inspired computing is a family of algorithms based on models of biological systems and behaviors. This talk will explore the wonders of these methods and the problems they can solve. Discover how genetic algorithms imitate the process of natural evolution to find the best solutions for given problems. Learn how genetic programming evolves computer programs to accomplish specific tasks. See how ant colony optimization mimics the way certain species of ants locate food and prioritize resources. Additionally, learn about particle swarm optimization, based on the behavior of flocks of birds, where individuals work together toward a common goal. We will also cover several frameworks and resources to help you get started.
Key takeaways:
* Understand the basic principles and concepts behind algorithms modeled after biological systems and behaviors
* Learn how genetic algorithms simulate natural evolution to identify optimal solutions for complex problems
* See real-world examples of how these bio-inspired methods can be applied across various industries to solve challenging problems
Based on my book 'Hands-On Genetic Algorithms with Python', 2nd edition
Evolving Explanations: Using Genetic Algorithms for Counterfactual Analysis in Explainable AI
Counterfactual explanations—answering the question “what would need to change for a different outcome?”—are among the most powerful tools in the Explainable AI toolbox. In this talk, we go beyond conventional methods and explore how genetic algorithms can be used to generate meaningful, actionable counterfactuals that provide insight into both data and the behavior of machine learning models.
Drawing from real-world scenarios and code examples from the German Credit Risk dataset, we’ll walk through how to:
* Use genetic algorithms to search for minimal, plausible input changes that flip model predictions.
* Evaluate and constrain counterfactuals for realism and interpretability.
* Detect potential model flaws and dataset biases through “what-if” scenario analysis.
Key Takeaways:
* Learn how to use genetic algorithms to generate counterfactual explanations that enhance model transparency and user trust.
* Discover how “what-if” analysis can reveal model weaknesses, dataset flaws, and actionable paths to desired outcomes.
* Gain practical insights into integrating counterfactuals into your AI workflows, with examples grounded in real-world data and Python code.
Audience:
This session is ideal for data scientists, ML practitioners, and AI educators interested in the intersection of optimization and explainability. Whether you're building transparent models or auditing black-box decisions, you’ll leave with tools and strategies to evolve your explanations — literally.
Level: Intermediate
Keywords: Explainable AI, Counterfactuals, Genetic Algorithms, Model Interpretability, Python, Optimization
Based on my book 'Hands-On Genetic Algorithms with Python', 2nd edition
Unlocking the Secrets of the Mystery-Word Game: A Journey Through NLP and Genetic Algorithms
This session introduces the basics of Natural Language Processing and word embeddings, highlighting their application in popular online games like Semantle. Discover how genetic algorithms can enhance game performance by creating an intelligent player that guesses the mystery word based on semantic similarity. We will explore the game's mechanics, learn the principles of genetic algorithms, and present a live demonstration of our AI player in action. Gain insights into the broader implications and future potential of integrating these advanced technologies. Join us to explore the innovative intersection of NLP, AI, and game design.
Key takeaways:
* Gain a foundational understanding of NLP and the concept of word embeddings, with a focus on their application in semantic similarity tasks.
* Discover how genetic algorithms can be employed to create a sophisticated player for the Mystery-Word game. Understand the principles behind genetic algorithms and their optimization capabilities.
Based on my book 'Hands-On Genetic Algorithms with Python', 2nd edition
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