Eric Siegel Speaking Fee: $10,000 to $20,000

Eric Siegel, Ph.D., is a renowned expert in the field of machine learning and predictive analytics. He is the founder of the influential Machine Learning Week conference series and the executive editor of The Machine Learning Times. Siegel's work bridges the gap between technology and business, making complex machine learning concepts accessible and actionable for organizations across various industries. His expertise has earned him a reputation as a leading consultant and speaker, having delivered over 110 keynote addresses worldwide. A former professor at Columbia University, Siegel has been recognized for his innovative teaching methods and has received the Distinguished Faculty Award. He authored the bestselling book "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die," which has been used in university courses globally. His second book, "The AI Playbook: Mastering the Rare Art of Machine Learning Deployment," further establishes his authority in the field by offering practical insights into deploying machine learning projects effectively. Siegel is also a Forbes contributor and has published numerous op-eds on analytics and social justice. His interdisciplinary approach not only focuses on the technical aspects of machine learning but also on its ethical implications and societal impact. His thought leadership extends to various media appearances, including Bloomberg TV, NPR, and National Geographic, where he discusses the transformative power of machine learning. With a dynamic and engaging presentation style, Eric Siegel captivates audiences by demystifying the complexities of machine learning and demonstrating its tangible benefits. His keynotes often include real-world case studies and practical applications, making his insights invaluable for businesses looking to leverage data-driven strategies. As an advocate for ethical AI, Siegel emphasizes the importance of responsible data use and the potential of machine learning to drive positive change.