(1)Title: Wayfair: Finding your perfect home with data and computational interior design
Tomer Weiss will share his experience as a data scientist and machine learning practitioner at Wayfair.
Machine learning is an important tool at Wayfair for addressing both customer, and business problems.
First, he will discuss a typical machine learning project’s life cycle, starting with the problem being address, and moving into how an initial solution was scoped and iterated on afterwards. Then, he will exemplify with his contributions to Wayfair’s computational interior design pipeline. The goal of such pipeline is to create a personalized shopping experience. For example, getting advice from your smartphone on which furniture items to pick and where to place them. Part of this work is also accepted as a paper to computer vision and artificial intelligence conferences.
He will conclude with future challenges and opportunities at this growing area.
Tomer Weiss defended his PhD in Computer Science at the University of California, Los Angeles. He received the Best Paper Award from the ACM SIGGRAPH conference on Motion in Games, for his work on virtual crowd simulation. He was a finalist presenter in both ACM SIGGRAPH Thesis Fast Forward, and the ACM SIGGRAPH Asia Doctoral Symposium in 2018. He received his MS in computer science from UCLA in 2016, and his BSc degree in computer science from Tel Aviv University in 2013. His research interests include computational design, mixed reality, and real-time methods for visual computing. Currently, he is a research scientist with Wayfair Inc.