Are You Vulnerable to Adversarial Machine Learning?

From Silos to Self-Service: Data Transformation at GE Aviation | Jon Tudor, GE Aviation

From Silos to Self-Service: Data Transformation at GE Aviation | Jon Tudor, GE Aviation

Are You Vulnerable to Adversarial Machine Learning? | Celeste Fralick, McAfee

Are You Vulnerable to Adversarial Machine Learning? | Celeste Fralick, McAfee

Augmented Human Intelligence | Walid Mehanna, Daimler

Augmented Human Intelligence | Walid Mehanna, Daimler

Democratizing Automated Forecasting | Lukas Stroemsdoerfer, Mercedes-Benz

Democratizing Automated Forecasting | Lukas Stroemsdoerfer, Mercedes-Benz

ABOUT

Think you can spot an attack with the naked eye? Think again! Attacks are becoming more nefarious and concealed. Adversarial Machine Learning (AML) is the study and design of AI algorithms that can resist attacks. Learn about poisoning and evasion, as well as how to spot and defend this model hacking using the latest analytics. Hitting images, text, and analytics that decide good or bad, lower your risk and vulnerability by implementing AML.

Celeste Fralick
Chief Data Scientist, McAfee

Celeste Fralick, Senior Principal Engineer and Chief Data Scientist for McAfee in the Office of the CTO, is responsible for innovating advanced analytics at McAfee. Named by Forbes on their inaugural list of “Top 50 Technical Women in America” and SC Media’s “Women in IT Security”, she has applied ML, DL, and AI to 10 different markets, spanning a nearly 40-year career. Celeste holds a Ph.D. in Biomedical Engineering from Arizona State University, concentrating in Deep Learning, Design of Experiments, and neuroscience.