Sponsor Round Table Session

Panel Title: AI and Data Systems: Synergies, Challenges, and Risks

Today AI promises to revolutionize business models and processes, restructure workforces, and transform data infrastructures to enhance process efficiency and improve decision-making throughout the enterprise. How important is really AI for data systems? Is it just AI hype or are we entering the era of AI-powered and AI-enabling data systems? Is Big Data too big without AI? And if data is the lifeblood of AI, do we have the data for the AI revolution in data systems?

We are entering a new era of technological innovations, breakthroughs and paradigms shifts. Will you stay behind?

Join us in a discussion with industry leaders on the field that will illuminate synergies and opportunities for AI and Data Systems, discuss challenges as well as unveil risks.

Panelists

Weiwei Gong is the Senior Manager of Data and In-Memory Technologies at Oracle. Passionate about hardware software co-design, Weiwei leads a team that builds performance-critical features of Oracle Database In-Memory. Her work has enabled efficient analytic query processing by leveraging emerging hardware technologies. Weiwei earned her M.S. from Renmin University of China, and Ph.D. from UMass Boston, both in Computer Science, and holds 7 patents that span various aspects of high-performance databases.

Delia David is a software engineer in the Data Infrastructure team at Facebook for the past 10 and a half years. Over the years she has built a distributed job scheduler running tens of millions of Analytical and Machine Learning jobs on a daily basis, extended Facebook’s Data Warehouse from 1 cluster to many datacenters and helped scale it from few petabytes to many exabytes. She has built Data Infra’s multi-region consistent Metadata system and implemented its Disaster Recovery solution. Over the last 2 years she has been working on adapting Data Infrastructure to the growing needs of Machine Learning, including designing the placement of exabyte-scale training data and ML jobs across many datacenters to best use our CPUs and GPUs to minimize costs and reduce job latency, implementing Feature Injection and Feature Reaping and helped design Facebook’s next generation datacenters for machine learning

Dr. Kai Zeng is a staff engineer at Alibaba Damo Academy. He received his Ph.D. in Computer Science from the University of California Los Angeles. Before joining Alibaba, he was a Senior Scientist at Microsoft Cloud and Information Service Lab, and a postdoc researcher at AMPLab, Univeristy of California Berkeley before that. He is committed to the research of large-scale distributed systems and database systems. He has published papers in top database journals and conferences (including SIGMOD, VLDB, ICDE, TODS, and so on). He has received the Best Paper Award in 2012 and the Best Demonstration Award in 2014 from SIGMOD and was nominated for this Best Demonstration Award in 2010.

Tianqing Wang is a senior engineer at Huawei company. His R&D interests include distributed systems, AI4DB, and DB4AI. He works on AI and DB for a long time and has published papers, patents, and books on the corresponding domain. Currently, he focuses on openGauss (an AI-Native and open-sourced database) to build a pioneering database system.

Moderators

Georgia Koutrika is a Research Director at Athena Research Center in Greece. She is an ACM Senior Member, IEEE Senior Member, and ACM Distinguished Speaker. She has more than 15 years of experience in multiple roles at HP Labs, IBM Almaden, and Stanford. Her work focuses on building novel technologies for exploring and leveraging data, including recommender systems and natural language interfaces, and has been incorporated in commercial products, described in 14 patents and 26 patent applications, and published in more than 90 papers in top-tier conferences and journals. She has received a PhD and a diploma in Computer Science from the Department of Informatics and Telecommunications, University of Athens in Greece. She is currently co-Editor-in-chief for VLDB Journal, PC co-chair for VLDB 2023 and for SoCC 2021, associate editor for SIGMOD2021 and VLDB2022, and ICDE2021 sponsorship chair.

Mohamed Sarwat is an assistant professor of computer science at Arizona State University. Dr. Sarwat is a recipient of the 2019 National Science Foundation CAREER award. His general research interest lies in developing robust and scalable data systems for spatial and spatiotemporal applications. The outcome of his research has been recognized by two best research paper awards in the IEEE International Conference on Mobile Data Management (MDM 2015) and the International Symposium on Spatial and Temporal Databases (SSTD 2011), a best of conference citation in the IEEE International Conference on Data Engineering (ICDE 2012) as well as a best vision paper award (3rd place) in SSTD 2017. Besides impact through scientific publications, Mohamed is also the co-architect of several software artifacts, which include Apache Sedona (a scalable system for processing big geospatial data) that is being used by major tech companies. He is an associate editor for the GeoInformatica journal and has served as an organizer / reviewer / program committee member for major data management and spatial computing venues. In June 2019, Dr. Sarwat has been named an Early Career Distinguished Lecturer by the IEEE Mobile Data Management community.