[CMU AI Seminar] Feb 8 at 12pm (Zoom) -- Huan Zhang (CMU) -- How We Trust a Black-box: Formal Verification of Deep Neural Networks -- AI Seminar sponsored by Morgan Stanley

[Asher Trockman]

Dear all,

Welcome to the CMU AI Seminar for the Spring 2022 semester!

We look forward to seeing you *next Tuesday (2/8)* from *1**2:00-1:00 PM
(U.S. Eastern time)* for the next talk of our *CMU AI seminar*, sponsored by
 Morgan Stanley <https://www.morganstanley.com/about-us/technology/>.

To learn more about the seminar series or see the future schedule, please
visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.

On 2/8, *Huan Zhang* (CMU) will be giving a talk titled "*How We Trust a
Black-box: Formal Verification of Deep Neural Networks*" to explain
state-of-the-art neural network verification techniques.

*Title*: How We Trust a Black-box: Formal Verification of Deep Neural
Networks

*Talk Abstract*: Neural networks have become a crucial element in modern
artificial intelligence. However, they are often black-boxes and can behave
unexpectedly and produce surprisingly wrong results. When applying neural
networks to mission-critical systems such as autonomous driving and
aircraft control, it is often desirable to formally verify their
trustworthiness such as safety and robustness. In this talk, I will first
introduce the problem of neural network verification and the challenges
involved to guarantee neural network output given bounded input
perturbations. Then, I will discuss the bound propagation based neural
network verification algorithms such as CROWN and beta-CROWN, which
efficiently propagate linear inequalities through the network in a backward
manner. My talk will highlight state-of-the-art verification techniques
used in our α,β-CROWN (alpha-beta-CROWN) verifier, a scalable, powerful and
GPU-accelerated neural network verifier that won the 2nd International
Verification of Neural Networks Competition (VNN-COMP’21) with the highest
total score.

*Speaker Bio*: Huan Zhang is a postdoctoral researcher at CMU, supervised
by Prof. Zico Kolter. He received his Ph.D. degree at UCLA in 2020. Huan's
research focuses on the trustworthiness of artificial intelligence,
especially on developing formal verification methods to guarantee the
robustness and safety of machine learning. Huan was awarded an IBM Ph.D.
fellowship and he led the winning team in the 2021 International
Verification of Neural Networks Competition. Huan received the 2021 AdvML
Rising Star Award sponsored by MIT-IBM Watson AI Lab.

*Zoom Link*:
https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09

Thanks,
Asher Trockman
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