AI & Machine Learning in Natural Hazards Engineering:
Technical & Modelling Q&A

November 6, 2018 | 1pm - 2pm PST


Artificial intelligence (AI) and machine learning (ML) have shown unprecedented success in a wide range of research areas and practical applications. In an earlier SimCenter webinar (“AI & Machine Learning in Natural Hazards Engineering: Approaches to Deep Learning”) held on October 2, Dr. Yu described a few such projects in her computer vision group at UC Berkeley, along with their goals for the SimCenter AI/ML team to empower future structural engineering and natural hazard modeling. She introduced the basic ideas and pipeline of deep learning approaches to a sample computer vision task, e.g. convolutional neural networks for image classification. This webinar will address technical and modeling questions from the audience community on how AI/ML could be used in your respective fields.


Stella Yu received her Ph.D. from Carnegie Mellon University, where she studied robotics at the Robotics Institute and vision science at the Center for the Neural Basis of Cognition. She continued her computer vision research as a postdoctoral fellow at UC Berkeley, and then studied art and vision as a Clare Booth Luce Professor at Boston College, during which she received an NSF CAREER award. Dr. Yu is currently the Director of Vision Group at the International Computer Science Institute (ICSI) and a Senior Fellow at the Berkeley Institute for Data Science (BIDS) at UC Berkeley. Dr. Yu is interested not only in understanding visual perception from multiple perspectives, but also in using computer vision and machine learning to capture and exceed human expertise in practical applications.

Webinar Registration

This webinar will address questions on applying deep learning to benefit current and pending projects where guidance on appropriate techniques or possible outcomes is needed. Please submit questions by Oct. 30, 2018 to or Slack to facilitate the Q&A format. Questions received by the 30th will be prioritized for the time allocated.