IAP Webinar Application Form: Topics in AI
Please complete this form to register for online attendance at the IAP Webinar on Thursday November 19, 2020 @ 11am-12pm PDT.

Speaker: Prof. Carole-Jean Wu, Arizona State and Facebook AI Research

Title: Deep Learning: It’s Not All About Recognizing Cats and Dogs

Abstract: In this webinar, I will talk about the underinvested deep learning personalization and recommendation systems in the overall research community. The training of state-of-the-art industry-scale personalization and recommendation models consumes the highest number of compute cycles among all deep learning use cases. For AI inference, personalization and recommendation consumes even higher compute cycles of 80%. What does state-of-the-art industry-scale neural personalization and recommendation models look like? I will present advancement on the development of deep learning recommender systems, the implications on system and architectural design and parallelism opportunities across the machine learning system stack over a variety of compute platforms. I will conclude with future directions on multi-scale system design and optimization.

Bio: Carole-Jean Wu is a Research Scientist at Facebook AI Research. Her research focus lies in the domain of computer system architecture with particular emphasis on energy- and memory-efficient systems. Her recent research has pivoted into designing systems for machine learning execution at-scale, such as for personalized recommender systems and mobile deployment. Carole-Jean chairs the MLPerf Recommendation Benchmark Advisory Board and co-chairs MLPerf Inference. Carole-Jean holds tenure as an Associate Professor at ASU. She received her M.A. and Ph.D. from Princeton and B.Sc. from Cornell. She is the recipient of the NSF CAREER Award, Facebook AI Infrastructure Mentorship Award, the IEEE Young Engineer of the Year Award, the Science Foundation Arizona Bisgrove Early Career Scholarship, and the Intel PhD Fellowship, among a number of Best Paper awards. She is a senior member of both ACM and IEEE.
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