In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We sat down with Abdelrahman Sayed Sayed to chat about his work on formal verification applied to autonomous vehicles. Could you tell us a bit about where you're studying and the broad topic of your research? My PhD topic is formal verification of neural ODE (ordinary differential equations) for safety evaluation in autonomous vehicles. Could you say something about formal verification and why it's such an important topic?

Moreover, when confined to a model architecture consisting of a single Neural ODE followed by a linear layer, T A-BN achieves 91.1% test

This challenge necessitates new mathematical and algorithmic tools that enable adaptation and zero-shot transfer.

Neural ODEs also provide atheoretical framework to study deep learning models from the continuous viewpoint, using the arsenal of ODE theory [40, 25, 41]. Importantly, they can also be seen as the continuous analog of ResNets.

Indeed, the results in Table 1, which shows13 the mean absolute percentage errors (MAPE), demonstrates this. The ac-14 curacy of neural ODE for the Poisson process is on par with our neural15 JSDE. However, for the Hawkes process (Exponential), Hawkes process16 (Power-Law), and self-correcting process, neural ODE gives much larger17 predictions errors. Forthesocial/medicaldatasets,weuseda20/64-24 dimensional latent state and parameterized the functions with two-hidden-layer MLPs with 32/64 hidden units. The time series modeling software that we used is designed for long event sequences and ignores the idle time after31 thelastevent.