For many, data is considered the 21st century’s most valuable commodity growing at an exponential rate – but is it for everyone? Analysts exploring data sets for insight, scientists looking for patterns, and consumers looking for information are just a few examples of user groups that need to access and dig into data. However, existing data exploration tools are falling behind in bridging the chasm between data and users, making data exploration intended only for the few. In this talk, we will discuss about what it takes to bridge this chasm and the new generation of intelligent data exploration tools that are emerging at the intersection of data management, natural language processing, machine learning and visualization. The talk will end with a summary of open questions on intelligent data assistants.

Georgia Koutrika is a Research Director at Athena Research Center in Greece. She has more than 15 years of experience in multiple roles at HP Labs, IBM Almaden, and Stanford. She has received a PhD and a diploma in Computer Science from the Department of Informatics and Telecommunications, University of Athens, Greece. Her work focuses on data exploration, recommendations, and data analytics, and has been incorporated in commercial products, described in 14 granted patents and 26 patent applications in the US and worldwide, and published in more than 90 papers in top-tier conferences and journals. Georgia is an ACM Senior Member, IEEE Senior Member, and ACM Distinguished Speaker. She is the editor of ACM SIGMOD Blog. Furthermore, her academic activities include: Editor-in-chief for VLDB Journal, PC co-chair for VLDB 2023, associate editor for TKDE, SIGMOD2021 and VLDB2022, ICDE2021 sponsorship chair.

Based on our experience conducting projects at the intersection of machine learning (ML) and interactive visualization (Vis), my talk will reflect on and discuss the current relation between these two areas. For that purpose, the talk’s structure will follow two main streams. First, I will talk about Vis for ML, that is, the idea that visualization can help machine learning researchers and practitioners gain interesting insights into their models. In the second part, I will then turn the relationship around and discuss how ML for Vis can guide visualization designers and analysts towards interesting visual patterns in the data. The talk will conclude with research challenges that lie ahead of us and that will pave the way for future interfaces between humans and data.

Michael Sedlmair is a professor at the University of Stuttgart, where he works at the intersection of human-computer interaction, visualization, and data analysis. Previously, Michael has worked at Jacobs University Bremen, University of Vienna, University of British Columbia, University of Munich (where he got his PhD), and the BMW Group Research and Technology. He also holds visiting positions at the Vienna University of Technology, and the Shandong University. His interests focus on information visualization, interactive machine learning, virtual and augmented reality, as well as the research and evaluation methodologies underlying them.