Keynote Speakers

Ion Stoica (UC Berkeley)

Sky Computing

Abstract

Technology ecosystems often undergo significant transformations as they mature. For example, telephony, the Internet, and PCs all started with a single provider, but each is now served by a competitive market that uses comprehensive technology standards to ensure compatibility. We believe that the cloud ecosystem, only fifteen years old, is on the verge of a similar transformation, driven by users desires for access to best-of-breed services and hardware, as well as enhanced availability, cost, performance, and security. In this talk, I will present our view on how todays cloud ecosystem could experience this transformation and discuss our early results and experiences in this endeavor.

Bio

Ion Stoica is a Professor in the EECS Department at the University of California at Berkeley, where he holds the Xu Bao Chancellor's Chair and is leading Sky Computing Lab (https://sky.cs.berkeley.edu/). He is currently doing research on cloud computing and AI systems. Current and past work includes Ray, Apache Spark, Apache Mesos, Tachyon, Chord DHT, and Dynamic Packet State (DPS). He is an ACM Fellow, Honorary Member of the Romanian Academy of Sciences, and has received numerous awards, including the Mark Weiser Award (2019), SIGOPS Hall of Fame Award (2015), and several "Test of Time" awards. He also co-founded several companies, including Anyscale (2019), Databricks (2013) and Conviva (2006).




Christos Kozyrakis (Stanford)

Cloud computing research in the era of machine learning

Abstract

The contemporary form of cloud computing is approaching its 18th anniversary. During this period, careful co-design of systems software and hardware brought substantial efficiency gains for both application developers and cloud operators. Most newly developed applications are now designed to be cloud native and there is a steady trend of migrating existing workloads to the cloud. This talk will focus on emerging challenges and opportunities for cloud computing research as we enter its adulthood phase. In particular, we will discuss how the growing prevalence machine learning workloads alters the demands placed on cloud systems. The most significant change is the necessity to broaden the co-design approach to encompass the applications themselves.

Bio

Christos Kozyrakis is a Professor of Electrical Engineering and Computer Science at Stanford University. He is also the faculty director of the Stanford Platform Lab. Christos specializes in computer architecture and systems software design. His current research focuses on cloud computing, systems for machine learning, and machine learning for systems. Christos holds a BS degree from the University of Crete and a PhD degree from the University of California at Berkeley. He is a fellow of the ACM and the IEEE. He has received the ACM SIGARCH Maurice Wilkes Award, the ISCA Influential Paper Award, the NSF Career Award, the Okawa Foundation Research Grant, and faculty awards by IBM, Microsoft, and Google.




Marc Brooker (AWS)

So Many Loops: The Dynamic Behavior of Distributed Systems

Abstract

The techniques and tools of distributed systems have formed the basis of the nearly two decades of phenomenal success of the cloud. But as we work to build ever-more-resilient systems, it has become clear that out our distributed computing toolkit is filled with deeply flawed tools, many of which make our task harder rather than easier. In this talk, we’ll consider the dynamic nature of distributed systems as control systems, explore the flaws in our current toolkit, and hope to find a panacea.

Bio

Marc Brooker is a Distinguished Engineer at Amazon Web Services, where he focusses on databases, serverless, and serverless databases. He’s particularly interested in building and operating large-scale systems, formal methods, system performance, and optimization. During his 15 years at AWS, Marc has worked on the teams behind EC2, Lambda, EBS, Firecracker, Aurora, and multiple other AWS products. He holds a PhD in Electrical Engineering from the University of Cape Town, South Africa.




Wang-Chiew Tan (Facebook AI)

Querying Unstructured and Structured Data with Large Language Models

Abstract

Recently, Large Language Models (LLMs) have emerged as a powerful tool for accessing parametric knowledge, but the potential of effectively tapping into the vast expanse of external or private data remains largely unexplored. This talk presents an open-source question-answering system for seamlessly integrating model parameters with knowledge from external data sources to enhance its predictive capabilities. Our larger vision transcends question answering. We envision a personal insight assistant, adept at sifting through one's past data to offer invaluable insights to help one make informed decisions and plan with foresight.

Bio

Wang-Chiew is a research scientist at Meta AI. Before she was the Head of Research at Megagon Labs, where she led the research efforts on building advanced technologies to enhance search by experience. Prior to joining Megagon Labs, she was a Professor of Computer Science at the University of California, Santa Cruz. She also spent two years at IBM Research - Almaden. She co-authored best papers, she is a co-recipient of the 2014 ACM PODS Alberto O. Mendelzon Test-of-Time Award, the 2018 ICDT Test-of-Time Award, and the 2020 Alonzo Church Award. She received the 2019 VLDB Women in Database Research Award, the 2021 National University of Singapore Outstanding Computing Alumni Award, and she is a Fellow of the ACM.

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