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Eric Siegel
Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI Applications Summit, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at hundreds of universities, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds…
Topics
Machine Learning for Healthcare Heal Thyself: Most Models Don't Deploy, But They Could
Industry leader Eric Siegel's latest research shows most models generated with machine learning to improve business operations in a new way never deploy. It turns out that machine learning operationalization – which changes existing processes in order to improve them – takes a lot more planning, socialization, and change-management efforts than most ever begin to realize. The problem is more in leadership than in technology. In this talk, Eric will outline the required practice needed to run ML projects so that they successfully deploy and deliver a business impact.
Algorithmic Bias in Healthcare Triage
Predictive models generated by machine learning can inadvertently disenfranchise groups that are already disenfranchised. Often called "machine bias" or "algorithmic bias", this has been demonstrated as it applies to predictive policing and credit scoring (loan approvals). In a nutshell, the false positive rates end up differing between protected groups. In this talk, "Predictive Analytics" author Eric Siegel will describe how this same phenomenon can arise for healthcare triage.
How Machine Learning Delivers on the Promise of AI
The excitement over machine learning and AI has reached a fever pitch. But what is the value, the function, the purpose? The most actionable win to be gained from data is prediction. This is achieved by analytically learning from data how to render predictions for each individual. Such predictions drive more effectively the millions of operational decisions that organizations make every day. In this keynote, Machine Learning Week founder and bestselling author Eric Siegel reveals how machine learning – aka predictive analytics – works and the ways in which it delivers value to organizations across industry sectors.
The AI Playbook: How to Capitalize on Machine Learning
The greatest tool is the hardest to use. Machine learning is the world’s most important general-purpose technology – but it’s notoriously difficult to launch. Outside Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing? A specialized business practice suitable for wide adoption. In this keynote, bestselling author Eric Siegel presents the gold-standard, six-step practice for ushering machine learning projects from conception to deployment. And he illustrates the practice with stories of success and of failure, including revealing case studies from UPS, FICO, and prominent dot-coms.
How Predictive Analytics Fortifies Healthcare
Predictive analytics addresses today’s pressing challenges in healthcare effectiveness and economics by improving operations across the spectrum of healthcare functions.
Most Machine Learning Projects Fail to Deploy – Here's the Remedy
Industry leader Eric Siegel's latest research shows most models generated with machine learning to improve business operations in a new way never deploy. It turns out that machine learning operationalization – which changes existing processes in order to improve them – takes a lot more planning, socialization, and change-management efforts than most ever begin to realize. The problem is more in leadership than in technology. In this talk, Eric will outline the required practice needed to run ML projects so that they successfully deploy and deliver a business impact.
How Machine Learning Reduces Risk in Financial Services
The gold standard method for leveraging data to reduce risk – in credit, insurance, and other lines of business – is machine learning. The predictive models this technology generates reduce risk, cut costs, and boost profit. In this keynote address, bestselling author and former Columbia University professor Eric Siegel will clearly demonstrate exactly what is learned from data and how enterprises apply what's learned to improve the business metrics that matter most in the financial services sector.
The High Cost of AI Hype
Machine learning has an “AI” problem. With new breathtaking capabilities from generative AI released every several months — and AI hype escalating at an even higher rate — it’s high time we differentiate most of today’s practical ML projects from those research advances. Including all ML initiatives under the “AI” umbrella oversells and misleads, contributing to a high failure rate for ML business deployments. In this keynote address, bestselling author Eric Siegel shows that, for most ML projects, the term “AI” goes entirely too far — it alludes to human-level capabilities. By unpacking the meaning of “AI,” he'll reveal just how overblown a buzzword it is.
Five Ways to Lower Costs with Machine Learning
Question: How does machine learning actively deliver increased returns? Answer: By driving operational decisions with predictive scores - one score assigned to each individual. In this way, an enterprise optimizes on what customers WILL do.
But, in tough times, our attention turns away from increasing returns, and towards decreasing costs. On top of boosting us up the hill, can machine learning pull us out of a hole? Heck, yes. Marketing more optimally means you can market less. Filtering high risk prospects means you will spend less. And, by retaining customers more efficiently, well, a customer saved is a customer earned - and one you need not acquire.
In this keynote, Eric Siegel will demonstrate five ways machine learning can lower costs without decreasing business, thus transforming your enterprise into a Lean, Mean Analytical Machine. You’ll want to run back home and break the news: We can’t afford not to do this.Uplift Modeling: Optimize for Influence and Persuade by the Numbers
Data driven decisions are meant to maximize impact - right? Well, the only way to optimize influence is to predict it. The analytical method to do this is called uplift modeling (aka, persuasion modeling). This is a completely different animal from standard predictive models, which predict customer behavior. Instead, uplift models predict the influence on an individual’s behavior gained by choosing one treatment over another. In this session, Machine Learning Week founder Eric Siegel provides an introduction to this growing area.
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What is Eric Siegel's speaking fee?
The typical range for Eric Siegel's speaking fee is $8,000–$20,000. The low end of the fee range represents their standard virtual fee. The high end of the fee range represents their standard fee for a US-based in-person speaking engagement. Appearances which require an extended schedule of activities or international travel will possibly exceed this fee range. Speaker fees update frequently. To receive a personalized quote for Eric Siegel to speak at your event, please consult with a Key Speakers advisor for up to date information and assistance.
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Other factors that influence a keynote speaker's fee include their reputation, expertise, demand, experience, duration of the presentation, travel requirements for the event location, and any additional services, activities or customization options they offer.
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Travel expenses are typically not included in the listed speaker's fee and are quoted separately. If the speaker needs to travel internationally, fees will be higher. These expenses may include airfare, accommodation, ground transportation at home and in the event city, and meals. We can provide more detailed information about travel expenses and arrangements once you've selected a speaker.
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