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
From Prediction to Intuition: Explainable AI with Counterfactuals and Genetic Search
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. They bridge the gap between abstract model reasoning and actionable insights. In this talk, we go beyond conventional methods and explore how genetic algorithms can evolve counterfactuals that are both realistic and actionable, offering fresh ways to understand data and model behavior.
Drawing from real-world scenarios and code examples using the German Credit Risk dataset, we’ll demonstrate 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 systematic “what-if” analysis.
Key Takeaways:
* Generate counterfactual explanations with genetic algorithms to enhance transparency and trust.
* Reveal model weaknesses and dataset flaws through structured “what-if” analysis.
* Integrate counterfactual techniques into real-world AI workflows with practical Python examples.
Audience:
This session is ideal for data scientists, ML practitioners, and AI educators who want practical, optimization-driven tools for explaining black-box models. Whether you’re designing responsible models, auditing decisions, or teaching interpretability, you’ll leave with strategies to evolve your explanations — literally.
Level: Intermediate
Keywords: Explainable AI, Responsible AI, Counterfactuals, Genetic Algorithms, Model Interpretability, Python, Optimization
Based on my book 'Hands-On Genetic Algorithms with Python', 2nd edition
Fast & Safe: Implementing Guardrails (and Caching Them) for Agentic LLM Apps
Enterprise LLM apps must screen inputs before they ever hit a model. This talk is a practitioner’s blueprint for incoming guardrails: malicious intent/hacking detection, toxicity filtering, out-of-scope routing, medical-emergency/self-harm risk with escalation, language detection, and PII redaction.
We show where each check sits in the request path, how to tune thresholds to avoid false blocks, and how to cache guardrail outcomes to reduce latency and save LLM tokens. We’ll cover fail-open vs. fail-closed strategies, audit logging, and metrics, with examples from real-world agentic applications
Learning Objectives
* Implement a layered incoming guardrail pipeline: malicious/hacking, toxicity, out-of-scope, emergency/self-harm, language, PII.
* Design escalation paths (human-in-the-loop) for medical/self-harm and clean out-of-scope handoffs.
* Cache guardrail decisions safely: key design, cohorts, similarity thresholds, TTLs, version-aware invalidation.
* Choose fail-open vs. fail-closed behaviors; place checks to minimize added latency and token usage.
* Instrument audits and dashboards (hit/miss, P95/P99, token savings) to prove safety and performance.
Tags:
LLM, Guardrails, Agentic, Agents, Caching, Token Optimization, Architecture, Observability, LangGraph
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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