Invited Talks

Dr. Lidong Zhou is a Corporate Vice President at Microsoft, Chief Scientist of the Microsoft Asia Pacific R&D Group, and Managing Director of Microsoft Research Asia. With a Ph.D. in Computer Science from Cornell University, Dr. Zhou’s career at Microsoft began in 2002. His journey has spanned across Microsoft’s Research labs, including the Silicon Valley lab as a researcher, the Redmond lab as a principal researcher and Research Manager of the Systems Research Group, and the Asia lab where he currently serves as the Managing Director since 2021. Dr. Zhou’s expertise lies in the field of computer systems research, particularly in developing scalable, reliable, and trustworthy distributed systems, with best papers at the ACM Symposium on Operating Systems Principles (SOSP), the USENIX Symposium on Operating Systems Design and Implementation (OSDI), and the USENIX Annual Technical Conference (ATC). He has played a pivotal role in the design and development of large-scale distributed systems, which are integral to various Microsoft products and services, from search engines and big data infrastructure to cloud systems and AI infrastructure.

Amin Vahdat is a Fellow and Chief Technologist for AI Infrastructure at Google, where his team is responsible for delivering industry-leading infrastructure which spans custom silicon, data centers, network, and supply chain and operations. This infrastructure serves Alphabet, Google and the world, and Artificial Intelligence technologies that empower ML developers and solve customers’ most pressing business challenges. In the past, he was Vice President and General Manager for Google's compute, storage, and network hardware and software infrastructure. Until 2019, he was the Technical Lead and Vice President for the Networking organization at Google.

I am a Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, and a Senior Research Director at Meta. My research focuses on empirical methods for natural language semantics, and involves designing machine learning algorithms, introducing new tasks and datasets, analyzing model performance, and, most recently, studying how to best develop self-supervision signals for pre-training. My honors include being elected as ACL President (2024), named an ACL Fellow (2021), as well as winning a PECASE award (2016), an Allen Distinguished Investigator award (2014), and many best paper awards. I was an undergraduate at NC State, recieved my PhD from MIT, and was a postdocal researcher at the University of Edinburgh.