Georgia Koutrika is Research Director at Athena Research Center in Greece, where she leads the DARELab. 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 in Greece. Her work focuses on natural language data interfaces, 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 100 papers in top-tier conferences and journals.
She is member of the VLDB Endowment Board of Trustees, co-EiC for VLDB Journal, PC co-chair for VLDB 2023, co-EiC of Proceedings of the VLDB (PVLDB). In the past, she has served or serves as associate editor for top conferences (such as ACM SIGMOD and VLDB) and journals (TKDE, VLDB Journal), as well as in various organization roles, including EDBT 2023 and ICDE 2021 sponsorship chair, and general chair for ACM SIGMOD 2016. Georgia is chair of the ACM working Group on Seasonal Schools, and member of the ACM-RAISE Working Group. She is ACM Senior Member, IEEE Senior Member, and ACM Distinguished Speaker.
If we are to satisfy the needs of casual users of data bases, we must break through the barriers that presently prevent these users from freely employing their native languages (Ted Codd, 1974)
Georgia has authored and co-authored more than 100 research papers and articles on natural language interfaces, personalization, recommendations, information extraction, entity resolution, and information integration, combining methods from databases, information retrieval, natural language processing and machine learning. For a complete list of publications, please visit DBLP. Citations of Georgia's work can be found in Google Scholar.
Georgia has given talks and tutorials on text-to-SQL systems, recommendations, personalization, and fairness.
Georgia's work has been incorporated in commercial products (HP, IBM, CourseRank), and is described in 14 granted patents and 26 patent applications in the US and worldwide. Partial lists of issued patents and patent applications can be found with the help of US Patent and Trademark Office (issued and published), and PatentBuddy ( here ).
Georgia has participated in several projects in the industry. Currently, she is the technical coordinator for INODE, an EU-funded H2020 project on intelligent data exploration, and member of the EOSC Future project , an EU-funded H2020 project that is implementing the European Open Science Cloud (EOSC) that will give European researchers access to a wide variety of research data and professionally provided services. .
Georgia is Editor-in-chief for VLDB Journal, Editor-in-Chief for PVLDB Vol 16, PC co-chair for VLDB 2023, a member of the VLDB Endowment Board of Trustees. She is an ACM SIGMOD Associate Information Director and editor of ACM SIGMOD Blog. She is an IEEE Senior member and ACM Senior member. She is EDBT 2023 sponsorship chair.
Georgia is teaching the course "Database Sysstems" for the MSc in Data Science & Information Technologies. She is also organizing a series of invited talks, that take place as part of the course.
Data is a prevalent part of every business and scientific domain, but its explosive volume and increasing complexity make data querying and exploration challenging even for experts. In an attempt to bridge the gap between users and data, text-to-SQL systems enable users to pose natural language queries over relational databases. We test their limits and build novel systems.
One of the biggest hurdles in today's exploration systems is that the system provides no explanations of the results or system choices. Nor does it trigger input from the user, for example, by asking the user to provide more information. We enable a conversational setting, wherethe system can explain results in natural language and can ask clarifications.
In a mixed-initiative setting, the system actively guides the user in what possible actions to perform or data to look at next. We are interested in recommendations in both cold-start (where the user has not given any input) and warm-start settings (where the user has asked one or more queries but may not know what to do next). In the formercase, the goal is to show a set of example or starter queries that the users could use to get some initial answers from the dataset. In the latter case, the system can leverage the users' interactions (queries) to show possible next queries.
As we increasingly depend on a variety of data-driven systems to assist us in many aspects of life, such as search engines and recommendation systems, we need to think about the fairness of such systems.
