Dr. Georgia Koutrika is a Research Director at the ATHENA Research Center in Greece. Prior to that, she was a senior research scientist at HP Labs in Palo Alto, CA, USA. She has also worked at IBM Research-Almaden in San Jose, CA, USA. She has been a post-doctoral research fellow at the Computer Science Dept., Stanford University, USA. She received a PhD in Computer Science, a MSc in Advanced Information Systems and a BSc in Computer Science from the Department of Informatics and Telecommunications, University of Athens, Greece.
Dr. Koutrika's work is in the broader area of big data and in the intersection of databases, machine learning and information retrieval, and it involves building innovative solutions for personalization and recommendation systems, user data analytics, large-scale information extraction, entity resolution and information integration, and data exploration. Her work has been incorporated in commercial products, has been described in 7 granted patents and 19 patent applications in the US and worldwide, and has been published in more than 80 research papers in top-tier conferences and journals. She has won two ACM SIGMOD Best Demo Awards and several industry recognitions for innovation, leadership and people&team development.
An IEEE Senior member and ACM member, Dr. Koutrika is an ACM SIGMOD Associate Information Director and editor of ACM SIGMOD Blog. She has served in various roles in top-tier conferences, including as Associate Editor for VLDB 2019, Demo PC co-Chair for ACM SIGMOD 2018, General co-Chair for ACM SIGMOD 2016, Industrial Track PC Chair for EDBT 2016, and Workshop and Tutorial co-Chair for IEEE ICDE 2016, and she is the organizer of several focused workshops in databases, personalization and data exploration.
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Deep learning in recommender systems
Deep learning methods have dramatically improved the state-of-the-art in computer vision, speech recognition, natural language processing (NLP) and many other domains. Deep learning started to...
Modern recommender systems in action (I know what movie you will watch in Netflix)
Recommender systems provide advice on items that may be of interest to a user (e.g., movies, products, travel, and leisure activities) by learning user preferences and relationships between users...
Modern recommender systems: matrices, bandits, and neurons
The proliferation of digital content in a plurality of forms (including e-news, movies, and online courses), along with the popularity of portable devices has created immense opportunities as well...
Multi-armed bandits in recommender systems
Traditional recommender systems can provide meaningful recommendations at an individual level by leveraging users' interests as demonstrated by their past activity. However, in many web-based...
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