Lab Home | Phone | Search
Center for Nonlinear Studies  Center for Nonlinear Studies
 Home 
 People 
 Current 
 Executive Committee 
 Postdocs 
 Visitors 
 Students 
 Research 
 Publications 
 Conferences 
 Workshops 
 Sponsorship 
 Talks 
 Seminars 
 Postdoc Seminars Archive 
 Quantum Lunch 
 Quantum Lunch Archive 
 P/T Colloquia 
 Archive 
 Ulam Scholar 
 
 Postdoc Nominations 
 Student Requests 
 Student Program 
 Visitor Requests 
 Description 
 Past Visitors 
 Services 
 General 
 
 History of CNLS 
 
 Maps, Directions 
 CNLS Office 
 T-Division 
 LANL 
 
Tuesday, December 19, 2023
09:00 AM - 10:00 AM
Webex

Seminar

Trustworthy AI with Scalable Formal Verification: A Linear Bound Propagation Approach

University of Illinois Urbana-Champaign - Electrical and Computer Engineering

Formal verification methods for deep neural networks (DNNs) aim to rigorously guarantee the trustworthiness of DNNs, ensuring properties like safety, security, robustness, and correctness even under malicious scenarios. These guarantees are essential for applying DNNs in mission-critical applications, such as autonomous systems and medical devices. In this talk, I will first introduce the concept of DNN verification and the problem formulation. Then, I will discuss a novel framework called "linear bound propagation methods" to enable efficient formal verification of large deep neural networks. By exploiting the structure of this problem, this framework achieves up to three orders of magnitude speedup compared to traditional algorithms and leads to the award-winning α,β-CROWN neural network verifier. Finally, I will discuss a few applications and potential usage of DNN verification, such as autonomous systems, non-linear control, and computer systems.

Bio: : Huan Zhang is an assistant professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC). Huan's work aims to build trustworthy AI systems that can be safely and reliably used in mission-critical tasks, with a focus on using formal verification techniques to give provable performance guarantees for machine learning. He leads a multi-institutional team developing the α,β-CROWN neural network verifier, which won VNN-COMP 2021, 2022, and 2023. He has received several awards, including an IBM Ph.D. fellowship, the 2021 Adversarial Machine Learning Rising Star Award, and a Schmidt Futures AI2050 Early Career Fellowship.

Host: Wenting Li