June 6, 2:00pm
Location: Santa Clara I

Panel Moderator: Adversarial Machine Learning: Lessons Learned, Challenges, and Opportunities

As artificial intelligence (AI) continues to advance in serving a diverse range of applications including computer vision, speech recognition, healthcare and cybersecurity, adversarial machine learning (AdvML) is not just a research topic, it has become a growing concern in defense and commercial communities. Many real-world ML applications have not taken adversarial attack into account during system design, thus the ML models are extremely fragile in adversarial settings. Recent research has investigated the vulnerability of ML algorithms and various defense mechanisms. The questions surrounding this space are more pressing than ever before: Can we make AI/ML more secure? How can we make a system robust to novel or potentially adversarial inputs? Can we use AdvML to help solve some of our industrial ML challenges? How can ML systems detect and adapt to changes in the environment over time? How can we improve maintainability and interpretability of deployed models? These questions are essential to consider in designing systems for high stakes applications. In this panel, we invite the IEEE community to join our experts in AdvML to discuss the lessons learned, challenges and opportunities in building more reliable and practical ML models by leveraging ML security and adversarial machine learning.

Catherine Huang has been a machine learning researcher since 2002. Her current research focuses on adversarial machine learning and AI-enabled cybersecurity. She has 17 patents and 40 papers. Previously, she was a Principal Engineer at McAfee and a Senior Research Scientist at Intel Labs. She directed adversarial machine learning research at Intel Science and Technology Center in University of California Berkeley. She received her Ph.D. in Biomedical Engineering at Oregon Health & Science University and M.S. in Electrical Engineering at University of New Brunswick. She was a Keynote Speaker at the MPOWER 2020 Executive Keynotes and IEEE SSCI 2016. She received the Best Application Paper Award at IEEE ICDIS 2022, the 2021 IEEE CIS Outstanding Organization Award, and the 2020 McAfee Values Program Award.