Abstract

Visualization is a powerful way to understand and interpret machine learning--as well as a promising area for ML researchers to investigate.In this talk, speaker will provide an introduction to the landscape of ML visualizations, organized by types of users and their goals. And discuss how each stage of the ML research and development pipeline lends itself to different visualization techniques: analyzing training data, understanding the internals of a model, and testing performance. In addition, may explore how visualization can play an important role in ML education and outreach to non-technical stakeholders.The talk will also include a brief introduction to key techniques from the fields of graphic design and human-computer interaction that are relevant in designing data displays. These ideas are helpful for refining existing visualizations, or inventing entirely new visual techniques.

Bio

Ryan Shen is a Data Scientist at NVIDIA, assist customers in building innovative solutions based on NVIDIA technology. His research interests are in the area of applying machine learning algorithms to real world problems. Prior to NVIDIA, Ryan was a ADAS engineer at UW EcoCar. He joined the visual driver awareness system team and developed various kinds of driver awareness HUD estimation modeling and object tracking projects centered around automotive area. Ryan receive the MS degree in NCKU EE department. During his MS program, he has worked as a visiting scholar at University of Washington in Seattle Multi Media Laboratory, working on NOAA Oceanic and Environmental Projects.

All faculties and students are welcome to join.