greece
Algorithms
Data Science
Artificial Intelligence
Title: Enhancing Entity Resolution and Retrieval through Dataset Decomposition
Speaker: Prof. Yannis Velegrakis (Utrecht University, The Netherlands)
Abstract: Any traditional data management process is typically applied to some dataset. We advocate that slicing the dataset and treating each slice differently can lead to better performance in a number of different scenarios. We present two works where this principle has been applied and we demonstrate its effectiveness. First, we show that even for short documents (forum posts), slicing allows for better discovery of documents related to a post at hand. Second, we illustrate how entity resolution can benefit from slicing. It is known that different entity resolution algorithms perform better on different datasets. We bring this idea within a dataset. We first slice the dataset and then select for each slice the method that performs best for each slice. Doing so leads to an improved overall performance of the entity resolution process. The two main challenges in this approach are to decide how to do the slicing, and how to select the resolution method that is best for a slice.
Short Biography: Yannis Velegrakis is a Computer Science professor at Utrecht University (Netherlands) where he holds the chair on Very Large Data Management, heads the Data Intensive Systems Group, and leads the Master’s programme in Data Science. His research area of expertise includes Data Preparation and Curation, Data Quality, Big Data Management, Knowledge Engineering, Graph Management, and Highly Heterogeneous Information Integration. He holds a PhD degree in Computer Science from the University of Toronto. He has been a professor at the University of Trento and a researcher at the AT&T Research Labs. He is also a PI at the Archimedes Unit of the Athena Research Center. He has spent time for research work at IBM Almaden Research Center, the Huawei European Research Center in Munich, the Center of Advanced Studies of the IBM Toronto Lab, the University of California, Santa-Cruz, and the University of Paris-Saclay. He has been the general chair of VLDB 2013 and ICDE 2024, the PC Chair of EDBT 2021, and area chair in multiple VLDB, SIGMOD, ICDE, and EDBT Conferences. He is currently serving on the board of the EDBT Association (as president), on the VLDB Board of Trustees, on the SIKS Research School board, and as associate editor for Systems on the SIGMOD Record editorial team.
To position Greece as a leading player in AI and Data Science
To build an AI Excellence Hub in Greece where the international research community can connect, groundbreaking ideas can thrive, and the next generation of scientists emerges, shaping a brighter future for Greece and the world
Welcome to ARCHIMEDES, a vibrant research hub connecting the global AI and Data Science research community fostering groundbreaking research in Greece and beyond. Its dedicated core team, comprising lead researchers, affiliated researchers, Post-Docs, PhDs and interns, is committed to advancing basic and applied research in Artificial Intelligence and its supporting disciplines, including Algorithms, Statistics, Learning Theory, and Game Theory organized around 8 core research areas. By collaborating with Greek and Foreign Universities and Research Institutes, ARCHIMEDES disseminates its research findings fostering knowledge exchange and providing enriching opportunities for students. Leveraging AI to address real-world challenges, ARCHIMEDES promotes innovation within the Greek ecosystem and extends its societal impact. Established in January 2022, as a research unit of the Athena Research Center with support from the Committee Greece 2021, ARCHIMEDES is funded for its first four years by the EU Recovery and Resilience Facility (RRF).
Archimedes, Athena Research Center, Greece is happy to see its work being featured at the 15th International Conference on Information Society and Technology (ICIST 2025), at Kaunas University of Technology, Lithuania, through an invited keynote speech by Dr. Serafeim Moustakidis, titled: “Bringing Biomechanics into the AI Era: A New Frontier for Gait and Joint Health.”
On Tuesday 21 October, 2025, from 1:00 pm to 2:00 pm, at the Archimedes Amphitheatre (1 Artemidos Street, 15125, Marousi, Archimedes, Athena Research Center, Greece), Professor Manos Athanassoulis, Associate Professor of Computer Science at Boston University (BU), USA, Director and Founder of the BU Data-intensive Systems and Computing Laboratory, BU, USA, and Co-Director of the BU Massive Data Algorithms and Systems Group, BU, USA, will deliver an Archimedes talk on "Harnessing the Untapped Benefits of Near-Sortedness for Data Systems."
The 16th Conference on Game Theory and AI for Security (GameSec25), an event strongly supported by Archimedes, Athena Research Center, Greece will take place at the National Technical University of Athens, Greece from October 13-15 2025. "With the rapid advancement of artificial intelligence, game theory, and security technologies, the resilience and trustworthiness of modern systems is more critical than ever. The 2025 Conference on Game Theory and AI for Security focuses on leveraging strategic decision-making, adversarial reasoning, and computational intelligence to address security challenges in complex and dynamic environments.
We are happy to announce that 11 papers from Archimedes, Athena Research Center, Greece, have been accepted at the Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025)!NeurIPS is a major annual event in the artificial intelligence and machine learning community and this year it will take place from December 2 to December 7 at the San Diego Convention Center, California, USA.
Vassilis Alimisis, a postdoctoral researcher at Archimedes, Athena Research Center, Greece, will deliver a tutorial on "Low-Power Analog Hardware Classifier Architecture for ML Applications: From A to Z" at the International Conference on Microelectronics (ICM 2025), taking place on 14-17 December, 2025, in Cairo, Egypt.Abstract: This tutorial provides a comprehensive introduction to the design and implementation of low-power analog hardware classifiers tailored for machine learning (ML) applications, with a focus on energy-constrained environments such as edge devices and biomedical wearables. With the growing need for fast, efficient, and real-time data processing, analog computing has re-emerged as a promising solution that enables in-memory computation and significantly reduces power consumption compared to conventional digital processors.
The project “ARCHIMEDES Unit: Research in Artificial Intelligence, Data Science and Algorithms” with code OPS 5154714 is implemented by the National Recovery and Resilience Plan “Greece 2.0” and is funded by the European Union – NextGenerationEU.
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