You're invited to the second lecture of the 2025 Spring Colloquium Series hosted by the AI Research Institute.

Event Registration: https://forms.gle/E42Y6HCXzA8WSUS39

(Offline attendees will receive a Nine Ounce Burger coupon)

• Registration Deadline: Monday, April 14

  • Only selected participants will receive a notification email after the deadline. Attendance at the offline lecture is limited to those who receive this notification.

  • If you register but cannot attend due to unforeseen circumstances, please notify us in advance to allow others to participate.

- Speaker: Professor Joonho Song (School of Interdisciplinary Studies / Department of Civil and Environmental Engineering, College of Engineering)
- Date & Time: Thursday, April 17, 2025, 4 PM
- Venue: Lecture Room, 1st Floor, Haedong Institute of Advanced Engineering, Building 303
- Live Broadcast:

YouTube: https://www.youtube.com/@AI-xi1ci
Zoom: https://us02web.zoom.us/j/85923628089?pwd=vi2qGZEWFgrDmiErimJm9GWRrOReVk.1
* ID: 859 2362 8089 / Password: 529164

"AI for Risk Intelligence in Smart Cities"

The cities we live in are rapidly evolving into cyber-physical systems, or 'smart cities,' where physical infrastructure is tightly integrated with information and data-driven technologies.

As a result, there is growing interest in 'risk intelligence,' which involves recognizing and proactively responding to various urban risks by integrating data and information and reasoning probabilistically using advanced tools.

This lecture will explore the potential advancements in smart city risk intelligence as a part of the AI+X field. It will also introduce recent research outcomes and practical applications of deep neural networks (DNNs) in the fields of structural engineering and seismic engineering.

Professor Joonho Song's research focuses on reliability analysis of structures and systems, optimization of reliability-based decision-making, resilience analysis of urban communities and infrastructure networks, seismic engineering, stochastic structural dynamics, and the application of machine learning and deep learning in structural engineering.