Invitation to the Workshop “From Systems to Trust: Data Management in the AI Era” | 15 September 2026
The European project DataGEMS and DareLab of the Athena Research Center cordially invite you to the workshop:
From Systems to Trust: Data Management in the AI Era
The workshop is dedicated to current trends and challenges in data management in the era of Artificial Intelligence. It will take place on Tuesday, September 15, 2026, from 10:00 AM to 3:30 PM, at the History Museum of the University of Athens.
The program is divided into two parts:
Part A: Research Presentations | 10:00 AM – 1:40 PM | in English
Eight presentations, organized into four thematic sessions, explore how Artificial Intelligence transforms data systems, enables natural language interaction, supports data search and linking, and highlights critical trustworthiness issues.
Part B: Open Panel Discussion | 2:30 PM – 3:30 PM | in Greek
“Artificial Intelligence at the Service of Society”
Who benefits, who takes the risk, and what does an open research infrastructure owe to the public that funds it? The panel is addressed to a broader audience and does not require having attended the first part.
Participation is free, but registration is required. The event will be held exclusively in person. You may choose to attend the first part, the second part, or both.
Registration: ai-dialogues.athenarc.gr
Πρόσκληση στην ημερίδα «From Systems to Trust: Data Management in the AI Era» | 15 Σεπτεμβρίου 2026
Το ευρωπαϊκό έργο DataGEMS και το DareLab του Ερευνητικού Κέντρου Αθηνά σας προσκαλούν στην ημερίδα:
From Systems to Trust: Data Management in the AI Era
Η ημερίδα είναι αφιερωμένη στις σύγχρονες τάσεις και προκλήσεις της διαχείρισης δεδομένων στην εποχή της Τεχνητής Νοημοσύνης και θα πραγματοποιηθεί την Τρίτη 15 Σεπτεμβρίου 2026, από τις 10:00 πμ έως τις 15:30 μμ, στο Μουσείο Ιστορίας του Πανεπιστημίου Αθηνών.
Το πρόγραμμα χωρίζεται σε δύο μέρη:
Μέρος Α΄: Ερευνητικές παρουσιάσεις | 10:00 πμ –13:40 μμ | στα αγγλικά
Οκτώ παρουσιάσεις, οργανωμένες σε τέσσερις θεματικές ενότητες, εξετάζουν πώς η Τεχνητή Νοημοσύνη μετασχηματίζει τα συστήματα δεδομένων, επιτρέπει την αλληλεπίδραση μέσω φυσικής γλώσσας, υποστηρίζει την αναζήτηση και σύνδεση δεδομένων και αναδεικνύει κρίσιμα ζητήματα αξιοπιστίας.
Μέρος Β΄: Ανοιχτή συζήτηση | 14:30 μμ –15:30 μμ | στα ελληνικά
«Η Τεχνητή Νοημοσύνη στην Υπηρεσία της Κοινωνίας»
Ποιος ωφελείται, ποιος αναλαμβάνει το ρίσκο και τι οφείλει μια ανοιχτή ερευνητική υποδομή στο κοινό που τη χρηματοδοτεί; Το πάνελ απευθύνεται σε ευρύτερο κοινό και δεν προϋποθέτει την παρακολούθηση του πρώτου μέρους.
Η συμμετοχή είναι δωρεάν, με απαραίτητη εγγραφή, και η εκδήλωση θα πραγματοποιηθεί αποκλειστικά με φυσική παρουσία. Μπορείτε να επιλέξετε να παρακολουθήσετε το πρώτο μέρος, το δεύτερο ή και τα δύο.
Εγγραφές: ai-dialogues.athenarc.gr
Hellenic Society for Computational Biology and Bioinformatics Conference, Kalamata, Greece
The Hellenic Society for Computational Biology and Bioinformatics (HSCBB) is pleased to announce the upcoming HSCBB26 Conference, which will take place in Kalamata, Greece, at the Municipal Cultural Center of Kalamata between 23-25 of October.
The conference will bring together researchers, academics, students, and professionals working at the interface of computational biology, bioinformatics, systems biology, artificial intelligence, and biomedical data science.
HSCBB26 will serve as a forum for presenting cutting-edge research, fostering scientific collaborations, and promoting interdisciplinary exchange across the rapidly evolving fields of computational life sciences. Participants will have the opportunity to attend keynote lectures, oral presentations, poster sessions, and networking activities covering a broad spectrum of topics related to computational and data-driven approaches in biology and medicine.
The conference aims to strengthen the Greek and international computational biology community by encouraging interaction among researchers from academia, research institutes, healthcare organizations, and industry.
We warmly invite researchers and students from relevant disciplines to participate in the conference and contribute to the advancement of computational biology and bioinformatics research.
More information, registration details, and updates are available at:
https://sites.google.com/view/hscbb26/home
MSc Thesis presentation of Violetta Gkika, Monday, 31/8/2026 at 15.00
On Monday, 31/8/2026 at 15.00, Violetta Gkika, a graduate student of the program “Data Science and Information Technologies, Biomedical Data Science – Bioinformatics” will present their MSc thesis titled:
Exploring Epithelial Heterogeneity in Pancreatic Ductal Adenocarcinoma Using the MLscAN Computational Framework
Malignant epithelial cells in pancreatic ductal adenocarcinoma (PDAC) exhibit a continuous range of phenotypes that appear to connect the traditional “classical” and “basal” transcriptional subtypes, suggesting high cellular plasticity rather than fixed binary categories. To explore this spectrum without forcing static labels based on gene markers alone, this thesis employs MLscAN (Machine Learning for single-cell ANalytics), an unbiased, unsupervised, probabilistic, and transition-aware computational framework developed by Prof. Manolakos’s team at NKUA. We applied the analysis to a high-resolution dataset of 106.110 malignant cells from 50 patients. Starting from a batch-integrated embedding that separates common epithelial features from patient-specific variation, the probabilistic approach robustly identifies the underlying transcriptional cancer states and their interactions, driven entirely by the data. This analysis was done in collaboration with Dr. Kalfakakou from Prof. Tsirigos’s lab at NYU Medical Center, who provided the dataset analyzed [1].
Unsupervised state inference resolved the epithelial landscape into five main transcriptional states. State-to-state trajectory modeling revealed two distinct evolutionary routes connecting the classical and basal programs. The analysis shows that both transitional paths start with a decline in classical identity markers like GATA6 and HNF4A, but then diverge. One path features late activation of the ∆Np63/TP63 squamous commitment cascade combined with cell proliferation. The other shifts toward a basal phenotype via a different, non-proliferative effector program that avoids the master-switch cascade altogether. By showing that the classical-to-basal transition involves at least two separate biological processes, our results advance the discussion of a multi-routed evolutionary model in tumor biology. Moreover, this study underscores the value of the MLscAN framework, illustrating how probabilistic, explicit modeling of state transitions can reveal complex evolutionary patterns often hidden by standard clustering methods.
[1] D. Kalfakakou, A. Tsirigos, et al. “Clonal Heterogeneity in Human Pancreatic Ductal Adenocarcinoma.” bioRxiv, preprint, 2025. DOI: 10.1101/2025.02.11.637729. Available: https://www.biorxiv.org/content/10.1101/2025.02.11.637729v1
Examination Committee:
Prof. Em. Elias S. Manolakos (advisor), Dept. of Informatics and Telecommunications, NKUA
Dr. Kostas Vekrellis, Research Director, Biomedical Research Foundation of the Academy of Athens
Prof. Dimitrios J. Stravopodis, Assoc. Professor of Biology, Department of Biology, NKUA
Zoom link
Elias Manolakos is inviting you to a scheduled Zoom meeting.
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MSc Thesis presentation of Mr. Giorgos Petsangourakis Tuesday, July 7, 2026
Student: “Giorgos Petsangourakis”
Program: “Data Science and Information Technologies”
Title: “Semantically-Guided Image Synthesis: Augmenting VAE Decoders with Foundation Model Representations”
Abstract: Variational Autoencoders (VAEs) serve as the critical first stage in modern latent generative modeling, yet they often face an inherent trade-off between latent compression and reconstruction fidelity.
Moreover, standard VAE architectures frequently struggle to recover fine-grained textures and complex semantic structures from the low-dimensional latent bottleneck z.
In this work, we propose an architectural enhancement to the VAE decoder that leverages high-level semantic information from Vision Foundation Models (VFMs). Central to our approach is the adaptation of the lightweight
convolutional semantic compressor introduced in the REGLUE framework. While REGLUE utilizes this module to entangle semantic features within a diffusion process, we extend its application to the VAE decoder to non-linearly aggregate multi-layer DINO VFM features into a spatially structured, low-dimensional representation that directly conditions the VAE decoder.
By injecting these “semantic maps” into the decoder’s upsampling blocks during finetuning, we provide the model with a structural signal that supplements the information in the primary latent space. Our experimental results demonstrate that this semantically-guided decoding strategy outperforms baseline VAEs across key metrics.
Most notably, we observe a substantial improvement in rFID (Reconstruction FID), indicating a superior ability to synthesize images that are both distributionally and structurally faithful to the ground truth. Furthermore, improvements in standard generative FID suggest that the augmented decoder provides a more robust foundation for downstream synthesis tasks.
Our findings highlight that the non-linear compression of VFM features is not only beneficial for diffusion backbones but is a transformative tool for overcoming the fundamental reconstruction bottlenecks of autoencoder architectures.
Date/Time: July 7, 2026 – 13:00 PM.
Examination Committee:
Dr. Bill Psomas
Dr. Stavros Perantonis
Dr. Ioannis Kakogeorgiou
Presentation link: https://meet.google.com/vsy-kkih-qah
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Bill Psomas
MSCA Postdoctoral Fellow
VRG, FEE, Czech Technical University in Prague
Karlovo nám. 13, 120 00 Nové Město, Czech Republic