Photo courtesy of 319studio

Keynote talks

Keynote Speakers

Ricardo Bianchini (Microsoft Research)

Serverless in Seattle: Toward Making Serverless the Future of the Cloud


The serverless computing paradigm has attractive properties, such as pay-per-use and fast scale-out. Unfortunately, it also has some key shortcomings that have so far limited its wide applicability. For example, current approaches for cold-start management either incur high latency or high resource overheads. As another example, running data-intensive applications in serverless platforms is currently slow or requires additional machinery. In this talk, I will describe our efforts towards understanding current serverless workloads, optimizing their cold start performance and efficiency, and broadening the scope of applications that can run efficiently in serverless platforms. Some of these efforts have already started transitioning to production in Azure Functions. I will conclude the talk with some open challenges going forward


Dr. Ricardo Bianchini is a Distinguished Engineer at Microsoft, where he leads efforts to improve the efficiency of the company's online services and datacenters. He also leads the Systems Research Group at Microsoft Research in Redmond. His main research interests include cloud and serverless computing, datacenter efficiency, and leveraging machine learning to improve systems. He has published nine award papers and received the CAREER award from the National Science Foundation. He has given several conference keynote talks and served on numerous program committees, including as Program Co-Chair of ASPLOS’18, EuroSys’17, and ICDCS’16. He is an ACM Fellow and an IEEE Fellow

Feifei Li (Alibaba)

Cloud Native Database Systems for Enterprise Applications


Cloud native database becomes increasingly important for the era of cloud computing, due to the needs for elasticity and high availability. A cloud native database system leverages software-hardware codesign to explore accelerations offered by new hardware such as RDMA and NVM. New design architectures such as shared-storage and shared everything enable a cloud native database to decouple computation from storage and provide elasticity. For highly concurrent workloads that require horizontal scalability, a cloud native database can also leverage a shared nothing layer to provide distributed query and transaction processing. Cloud applications also require that cloud native databases to offer high availability through distributed consensus protocols. Furthermore, The need for integrating big data processing workloads with a traditional OLAP data warehouse inside a cloud native database/data warehouse becomes increasingly imperative, so that the system can provide both real time online interactive analytics and batched offline ETL with complex computation. We will present key technical ideas towards building cloud native database systems, as well as the application and integration and big data and AI techniques in such systems


Feifei Li is currently a Vice President of Alibaba Group, ACM Distinguished Scientist, President of the Database Products Business Unit of Alibaba Cloud Intelligence, and Director of the Database and Storage Lab of DAMO academy. He has won multiple awards from ACM and IEEE and others. He is a recipient of the ACM SoCC 2019 Best Paper Award Runner-up, IEEE ICDE 2014 10 Years Most Influential Paper Award, ACM SIGMOD 2016 Best Paper Award, ACM SIGMOD 2015 Best System Demonstration Award, IEEE ICDE 2004 Best Paper Award. He has been an associate editor, PC co-chairs, and core committee members for many prestigious journals and conferences, and has led the R&D efforts of building cloud native database systems and products at Alibaba

Juliana Freire (New York University)

Towards Usability, Interactivity, and Trust for Data-Intensive Computations


The abundance of data, coupled with cheap and widely-available computing and storage, has revolutionized science, industry and government alike. Now, to a large extent, the bottleneck to actionable insights lies with people. From a data management and systems perspective, this leads to several challenges including the need to build usable and scalable tools, to support the interactivity requirements for exploratory analyses, and to guide the users in data discovery and exploration. In this talk, I will present a set of techniques that aim to address these challenges by combining methods from multiple areas of computer science. I will also describe systems we have built that use these techniques to empower domain experts to explore data and build trust in the insights they derive.


Juliana Freire is a Professor at the Department of Computer Science and Engineering at New York University. She also holds an appointment in the Courant Institute for Mathematical Science and is a faculty member at the NYU Center of Data Science. Her research interests are in large-scale data analysis, visualization, and provenance management. An important theme is Professor Freire's work is the development of data management techniques and infrastructure to address problems introduced by emerging applications. Recently, her work has focused on the analysis and visualization urban, scientific and Web data. Within scientific data management, she is best known for her work in provenance and computational reproducibility, and for being a co-creator of the open-source VisTrails system ( Professor Freire is an active member of the database and Web research communities, having co-authored over 130 technical papers and holding 9 U.S. patents. She has chaired or co-chaired several workshops and conferences, and has participated as a program committee member in over 60 events. She has received several awards, including an NSF CAREER, an IBM Faculty award, and a Google Faculty Research award. Her research has been funded by grants from the National Science Foundation, Department of Energy, National Institutes of Health, University of Utah, NYU, Sloan Foundation, Betty Moore Foundation, Google, Amazon, Microsoft Research, Yahoo! and IBM.

Latest news