[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]

Hi all,

Just a reminder that the CMU AI Seminar <http://www.cs.cmu.edu/~aiseminar/> is
tomorrow *12pm-1pm*:
https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09.

*Huan Zhang (CMU)* will be talking about his award-winning neural network
verification techniques, as well as neural network verification more
generally.

Thanks,
Asher

On Fri, Feb 4, 2022 at 12:41 PM Asher Trockman <ashert at cs.cmu.edu> wrote:

> 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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