June 6, 11:00am
Location: Magnolia

Panelist: Artificial Intelligence for Autonomous Driving

Autonomous driving is one of the fastest-growing industries leveraging artificial intelligence solutions. Autonomous driving has been using a suite of modalities like cameras, LiDARs, RADARs, microphones, ultrasonics, city-traffic data, and everything around in order to bring autonomous cars to a boring reality. This panel will bring together experts in the field of AI for autonomous driving to discuss the frontiers of Perception; the field of distilling sensor data into representations understandable by the autonomy stack. The panel is comprised of a diverse group with several years of experience in building robots and complex perception systems for the purpose of autonomous passenger vehicles and delivery robots. This panel will discuss challenges to developing a scalable, safe, and ethical perception system for the future. Topics will include, but are not limited to long tail problems in autonomous driving, data mining, perception architectures, ML Infrastructure, and future technologies, among others. The panelists will provide their viewpoint not only from a performance perspective but from the lens of an experienced practitioner balancing reliability with practical computing considerations. This is an excellent opportunity for attendees to gain a deeper understanding of the latest advancements in AI for autonomous driving and the pivotal role it will play in reshaping our transportation landscape.

Aleksandr Petiushko is a Technical Lead Manager at Machine Learning Research at Nuro, leading the Autonomy Interaction Research team. He is also a lecturer at Lomonosov Moscow State University, giving courses on the Theory of Deep Learning. He has received M.Sc. in 2006 and Ph.D. in 2016. Before Nuro, he worked as a Principal Engineer / Scientific Expert at Huawei, as Managing Director / Leading Researcher at Artificial Intelligence Research Institute (AIRI), and organized and led the collaboration activities between Academia and Industry. His research interest lies in the application of empirical and theoretical robustness techniques to different tasks, and related works have been accepted by different top conferences (ECCV, AAAI, IJCAI, NeurIPS).