NEWS

14 October 2025

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."

9 October 2025

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.

7 October 2025

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.

6 October 2025

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.