Date: 31-October-2025
Time: 1:00 PM ET (New York Time)
Presenter: Dr. Olaf Sporns
Meeting information:
Meeting number: 2531 265 9090
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Access code: 253 126 59090
Date: 20-November-2025
Time: 09:30 AM ET (New York Time)
Presenter: Dr. Lin Zhang
Based on the IEEE Xplore® article titled: “The Partial Spoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance”
Published: IEEE/ACM Transactions on Audio, Speech, and Language Processing, December 2022.
Date: 09-December-2025
Time: 09:30 AM ET (New York Time)
Presenter: Dr. Magdalena Fuentes
About this topic:
Date: 02-December-2025
Time: 9:00 AM ET (New York Time)
Presenter: Dr. André L. F. de Almeida
About this topic:
Date: 18-December-2025
Time: 10:00 AM ET (New York Time)
Presenter: Dr. Luca Barbieri
Based on the IEEE Xplore® article under the title “A Tiny Transformer-Based Anomaly Detection Framework for IoT Solutions”
Published: IEEE Open Journal of Signal Processing, November 2023.
Download article: Original article is open access and publicly available for download: ARTICLE LINK
Abstract
Date: 06-November-2025
Time: 08:00 AM ET (New York Time)
Presenters: Dr. Juan Tapia & Dr. Daniel Benalcazar
Based on the IEEE Xplore® article: “Synthetic ID Card Image Generation for Improving Presentation Attack Detection”
Published: IEEE Transactions on Information Forensics and Security, March 2023
Download article: Original article is open access and publicly available for download: ARTICLE LINK
Abstract
Date: 07-November-2025
Time: 08:00 AM ET (New York Time)
Presenters: Mr. Alexander Karataev, Mr. Christian Forsch & Dr. Laura Cottatellucci
Based on the IEEE Xplore® article under the same title
Published: IEEE Open Journal of Signal Processing, January 2024.
Download article: Original article is open access and publicly available for download.
ARTICLE LINK
Abstract
Date: 05-November-2025
Time: 10:30 AM ET (New York Time)
Presenter: Dr. Ana Perez-Neira
Abstract
AI keeps getting bigger—but does bigger always mean better? As Large Language Models (LLMs) take center stage, their enormous resource demands raise pressing concerns about sustainability and efficiency. In this webinar, the presenter questioned the assumption that AI must be massive to be powerful.
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