• May 18, 2021: Our demo "DatAgent: The Imminent Age of Intelligent Data Assistants", has been accepted at VLDB 2021 (with A. Mandamadiotis, S. Eleftherakis, A. Glenis, D. Skoutas, Y. Stavrakas).
  • May 10, 2021: Our tutorial on "Fairness-aware Methods in Rankings and Recommenders" has been accepted at ACM MDM 2021 (with E. Pitoura and K. Stefanidis).
  • April 8, 2021: "The Rise of Intelligent Data Assistants", Invited talk at the Trustworthy Data Science and AI Webinar Series, Simon Fraser University.
  • March 28, 2021: "Computer Science: unicorns, dragons and heroes". I am moderating this cool panel at the Athens Science Festival 2021 (online).
  • March 23, 2021: "The Rise of Intelligent Data Assistants: Democratizing Data Access" , Keynote at BigVis2021@EDBT.
  • February 2021: Our paper on "An In-Depth Benchmarking of Text-to-SQL Systems" has been accepted at ACM SIGMOD 2021 (with O. Gkini, T. Belmpas, and Y. Ioannidis).
  • February 2021: Our demo "PyExplore: Query Recommendations for Data Exploration without Query Logs" has been accepted at ACM SIGMOD 2021 (with A. Glenis).
  • February 2021: Our tutorial on A Deep Dive into Deep Learning Approaches for Text-to-SQL Systems has been accepted at ACM SIGMOD 2021 (with G. Katsogiannis-Meimarakis).
  • January 2021: Our tutorial on Deep Learning Approaches for Text-to-SQL Systems has been accepted at EDBT 2021 (with G. Katsogiannis-Meimarakis).
  • Diversity and Inclusion in Database Conference Venues
  • The Greek ACM-W Chapter



Georgia has authored and co-authored more than 90 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, a project on intelligent data exploration.

Professional Activities

Georgia is an IEEE Senior member and ACM Senior member, and ACM Distinguished Speaker. She is an ACM SIGMOD Associate Information Director and editor of ACM SIGMOD Blog. She is Editor-in-chief for VLDB Journal, PC co-chair for VLDB 2023, associate editor for TKDE, SIGMOD and PVLDB. She is ICDE2021 sponsorship chair.


Natural Language Queries

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.