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Put AI to work
8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK
Pin-Yu Chen

Pin-Yu Chen
Research Staff Member, IBM Research AI

Website

Pin-Yu Chen is a research staff member in the AI Foundations Learning Group at the IBM Thomas J. Watson Research Center in Yorktown Heights, NY. His recent research focuses on adversarial machine learning and robustness analysis of neural networks; he’s also interested in graph and network data analytics and their applications to data mining, machine learning, signal processing, and cybersecurity. Pin-Yu received the NIPS 2017 Best Reviewer Award and the IEEE GLOBECOM 2010 GOLD Best Paper Award as well as several travel grants, including IEEE ICASSP 2014 (NSF), IEEE ICASSP 2015 (SPS), IEEE Security and Privacy Symposium, NSF Graph Signal Processing Workshop 2016, and ACM KDD 2016. He is a member of the Tau Beta Pi Honor Society and the Phi Kappa Phi Honor Society and was the recipient of the Chia-Lun Lo Fellowship from the University of Michigan Ann Arbor. Pin-Yu holds a BS in electrical engineering and computer science (undergraduate honors program) from National Chiao Tung University, Taiwan, an MS in communication engineering from National Taiwan University, Taiwan, and an MA in statistics and a PhD in electrical engineering and computer science, both from the University of Michigan, Ann Arbor.

Sessions

16:00–16:40 Wednesday, 10 October 2018
How CLEVER is your neural network? Robustness evaluation against adversarial examples
Models and Methods
 Location: King's Suite - Sandringham
Secondary topics:  Computer Vision, Deep Learning models, Ethics, Privacy, and Security, Retail and e-commerce
Pin-Yu Chen (IBM Research AI)
Average rating: *****
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Neural networks are particularly vulnerable to adversarial inputs. Carefully designed perturbations can lead a well-trained model to misbehave, raising new concerns about safety-critical and security-critical applications. Pin-Yu Chen offers an overview of CLEVER, a comprehensive robustness measure that can be used to assess the robustness of any neural network classifiers. Read more.