Abraham Kang

This talk will give listeners a deep understanding of machine learning adversarial examples.  Similar to fuzzing, adversarial samples/examples exploit weaknesses in how input is processed. Adversarial Examples are an inherent weakness in almost every machine learning model. Come to this talk if you are interested in learning more about MLSec and how your ML applications may be at risk.

Abraham Kang is fascinated with the nuanced details associated with machine learning algorithms, programming languages and their associated APIs. Kang has a BS from Cornell University. He is currently doing research in security for machine learning.  He worked for Samsung Research America as a Senior Director Software helping to drive secure development across AI, mobile, payments and VR. Prior to joining Samsung, he worked as Principal Security Researcher for HP in their Software Security Research group. He is focused on machine learning/AI, web, application frameworks, blockchain smart contracts, intelligent assistants, and mobile security and has presented his findings at RSA USA, Black Hat USA, DEFCON, OWASP AppSec USA, and BSIDES.