Abstract: Scene understanding is a major challenge for autonomous systems. It requires combining diverse information sources, background knowledge, and different sensor data to understand the physical and semantic aspects of dynamic environments. Current technology for scene understanding relies heavily on computer vision and deep learning techniques to perform tasks such as object detection and localization. However, due to the complex and dynamic nature of driving scenes, the technology's complete reliance on raw data poses challenges, especially with respect to edge cases. In this talk, I will discuss some of these challenges along with how they are currently being handled. Next, I will discuss a novel perspective that we have introduced as part of my dissertation, which leverages the use of external knowledge, representation learning, and neurosymbolic AI to address some of these challenges. Finally, I will share my thoughts on directions for future research and new applications and domains where we can apply this technology to improve machine perception in autonomous systems. 

Bio: Ruwan Wickramarachchi is a Ph.D. candidate at the AI Institute, University of South Carolina. His dissertation research focuses on introducing expressive knowledge representation and neurosymbolic AI techniques to improve machine perception and context understanding in autonomous systems. He has published several research papers, co-invented patents, and co-orgnaized multiple tutorials on neurosymbolic AI and its emerging uses in addressing scene understanding challenges in autonomous systems. Prior to joining the doctoral program, he worked as a senior engineer in the machine learning research group at London Stock Exchange Group (LSEG). 

Location: SWGN 2A27

We would love in-person attendance (required for registered students),
but remote attendance is possible on Zoom:

https://us06web.zoom.us/j/8440139296?pwd=b09lRCtJR0FCTWcyeGtCVVlUMDNKQT…

Meeting ID: 844 013 9296
Passcode: 12345