2910, 2026

Course and Master’s Thesis Registrations for the Winter Semester 2026-27

Course and Master’s Thesis Registrations for the Winter Semester 2026-27

Δηλώσεις μεταπτυχιακών μαθημάτων και Διπλωματικών Εργασιών Χειμερινού εξαμήνου 2026-27
• Καλούνται οι μεταπτυχιακοί φοιτητές ΜΕ ΕΤΗ ΕΙΣΑΓΩΓΗΣ 2018, 2019 ΚΑΙ 2020 τoυ ΔΠΜΣ:
Data Science and Information Technologies
να δηλώσουν έως 5 μαθήματα χειμερινού εξαμήνου ή/και Διπλωματική Εργασία, από τις 29.10.2026 ως τις 26.11.2026 στο  Πληροφοριακό Σύστημα Διαχείρισης Μεταπτυχιακών Σπουδών:
https://pgs.di.uoa.gr
• Καλούνται οι μεταπτυχιακοί φοιτητές ΜΕ ΕΤΗ ΕΙΣΑΓΩΓΗΣ 2021, 2022, 2023, 2024,2025 και 2026 του ΔΠΜΣ να δηλώσουν έως 5 μαθήματα χειμερινού εξαμήνου ή/και Διπλωματική Εργασία, από τις από τις 29.10.2026 ως τις 26.11.2026 στην Πύλη Φοιτητολογίου του ΕΚΠΑ:
https://my-uni.uoa.gr/
• Η δήλωση μαθημάτων είναι υποχρεωτική για όλους τους μεταπτυχιακούς φοιτητές.

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TRANSLATED – FOR INTERNATIONAL STUDENTS
Athens, 29.10.2026
Course and Master’s Thesis Registrations for the Winter Semester 2026-27

• Postgraduate students YEARS OF ADMISSION 2021, 2022, 2023, 2024, 2025, and 2026 of the following MSc and Interinstitutional MSc program:
• Data Science and Information Technologies
are invited to register for up to 5 winter semester courses and/or a Master’s Thesis, from 29.10.2026 to 26.11.2026, in the Postgraduate Studies Management Information System: https://pgs.di.uoa.gr
• Course registration is mandatory for all postgraduate students.

Registration is submitted through the NKUA Student Portal (My Uni):

https://my-uni.uoa.gr/
From Secretariat of NKUA Department of Informatics and Telecommunications. Email: secretpm@di.uoa.gr

1910, 2026

MSc Thesis presentation of Dimitrios Krikonis, Wednesday, October 21, 2026

Student: Dimitrios Krikonis
Program: Data Science and Information Technologies / Big Data and
Artificial Intelligence

Title:
Machine Learning for Archaeology: A Human-in-the-Loop Chronology
Prediction System for Pottery from the Athenian Agora

Abstract:
This thesis investigates the use of machine learning as a decision-support
tool for archaeological pottery dating, aiming to provide initial
estimates without replacing expert judgement. First, it conducts a
comparative evaluation of text- and image-processing methods and different
classification and regression algorithms, using 1,910 pottery items from
the The Athenian Agora publication series. Five feature extraction methods
are compared across two modalities: TF-IDF and BERT for text, Canny with
HOG, ResNet and ViT for images, along with their six multimodal
combinations, giving eleven feature sets in total. The results show that
text-based methods outperform image-based approaches, while combining the
two does not provide a reliable improvement, possibly due to the limited
availability of photographs. TF-IDF performs better than BERT on the
dataset, and Vision Transformer (ViT) achieves the strongest results among
image representations. Additionally, a web application was designed and
developed (https://dimitris-kr.github.io/pottery-chronology-predictor/)
that integrates the selected models into a predict–validate–retrain
workflow, allowing archaeologists to review predictions, examine their
uncertainty, validate or correct chronologies, and contribute data for
future model retraining. The system was evaluated by its intended users,
who successfully completed the full workflow. However, whether successive
retraining cycles improve predictive performance remains an open question,
requiring additional validated data collected over time and a dedicated
evaluation methodology.

Date/Time: October 21, 2026 – 13:05 PM EEST

Examination Committee:
Maria Roussou (supervisor), Associate Professor, University of Athens
Alexandros Ntoulas, Assistant Professor, University of Athens
Ioannis Panagakis, Associate Professor, University of Athens

Presentation Link:
https://uoa.webex.com/meet/mariar

1910, 2026

MSc Thesis presentation of Eleni Panagiotopoulou Thursday 22/10/2026, 3pm

On Thursday 22/a0/2025 at 3 pm, Eleni Panagiotopoulou, a graduate student in the program “Data Science and Information Technologies, Biomedical Data Science – Bioinformatics” will present their MSc thesis titled:

Actigraphy Feature Representations for Mild Cognitive Impairment Classification: Comparing Summary, Circadian and Wavelet Features across Sensitivity–Specificity Operating Points
Mild cognitive impairment (MCI) is characterized by cognitive deficits that exceed those expected with normal aging without substantially compromising independence in everyday activities. Actigraphy provides a non-invasive means of monitoring rest-activity patterns and may offer additional information for identifying cognitive impairment. This thesis investigated how different actigraphy-derived feature representations distinguish participants with MCI from those with normal cognition.
We compared three feature families: summary statistics, combined cosinor and non-parametric rest-activity descriptors, and discrete wavelet transform (DWT) features. An integrated software pipeline supported signal preprocessing, feature extraction, feature selection, and evaluation of multiple classical machine-learning algorithms within a repeated nested cross-validation framework. We compared configurations that met predefined performance criteria using recall-specificity Pareto fronts and parsimony-based selection. Additional analyses examined age-only models, matched signal lengths, time-of-day windows, model complexity, and hyperparameter tuning.
The feature families supported different recall-specificity trade-offs. Summary statistics achieved the highest median recall, reaching 0.818 at a median specificity of 0.700. Combined cosinor and non-parametric descriptors achieved recall of 0.800 and specificity of 0.750, while DWT features achieved the highest specificity (0.850) with recall of 0.727. Age-only models provided a competitive reference, but incorporating actigraphy features improved the attainable trade-offs. Compact actigraphy representations for wavelet feature extraction retained high specificity, although with lower recall than the leading full-series configuration. Simpler models and configurations without hyperparameter tuning also achieved competitive performance.
These findings indicate that the preferred actigraphy representation depends on the sensitivity-specificity balance sought, rather than a single feature family being universally superior. The thesis contributes an extended analysis pipeline and a systematic comparison of complementary activity representations for MCI classification. The results encourage further investigation of actigraphy as a source of information for clinical decision support, while the limited cohort and exploratory configuration-selection process necessitate independent validation before clinical application.

Examination Committee:

Prof. Em. Elias S. Manolakos (advisor), Dept. of Informatics and Telecommunications, NKUA
Dr. Nikolaos Scarmeas, Professor of Neurology, Department of Medicine, NKUA
Prof. Dimitrios J. Stravopodis, Assoc. Professor of Biology, Department of Biology, NKUA
Meeting Details
Elias Manolakos is inviting you to a scheduled Zoom meeting.
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1510, 2026

MSc Thesis presentation of Ms. Myrto Potamiti Friday, October 16, 2026

Student: “Myrto Potamiti”

Program: “Data Science and Information Technologies”

Title: “MSTOK: Adapting Discrete Visual Tokenization to Multispectral Earth Observation Imagery”
Abstract:
Recent advances in generative modelling for Earth observation highlight the importance of compact image representations that preserve spatial and spectral information. This thesis presents MSTok, an adaptation of the Open-MAGVIT2 visual tokenizer using lookup-free quantization (LFQ) for 12-band Sentinel-2 Level-2A imagery from TerraMesh. Experiments investigate reflectance preprocessing, discrete code-space capacity, ImageNet-pretrained initialization, architectural adaptations, and joint ImageNet–TerraMesh training. Reconstruction quality is assessed
on 89,088 validation samples at 224×224 resolution using multispectral and RGB perceptual metrics. After one epoch, the standard model trained from scratch outperformed its
ImageNet-initialized counterpart across the reported reconstruction metrics, achieving an FID of 61.38 versus 69.53. A recurring trade-off emerged between multispectral accuracy and RGB perceptual quality: extending linear-adapter training reduced all-band MAE from 0.0480 to 0.0469, while increasing FID from 29.26 to 39.18. High code utilization and accurate standalone adapter reconstruction did not ensure effective end-to-end tokenization. The from-scratch configuration was retained as the preferred overall model within the available training budget.
These findings demonstrate the feasibility of LFQ-based multispectral tokenization and the need to assess multispectral reconstruction fidelity and visible-band perceptual quality jointly.

Date/Time: October 16, 2026 – 14:00 PM.
Examination Committee:
”
Dr. Bill Psomas
Dr. Stavros Perantonis
Dr. Ioannis Kakogeorgiou
“
Presentation link:
https://meet.google.com/dvs-gzfy-nkq

810, 2026

MSc Thesis Presentation by Asimenia Giagli

MSc Thesis Presentation by Asimenia Giagli titled: “A Reproducible Pipeline for Prioritizing Antibody-Accessible Cell-Surface Targets from Single-Cell Transcriptomic Data”

Abstract:
Cell-surface proteins are promising candidates for antibody-based therapies, but their prioritization from transcriptomic data requires more than differential expression alone. This thesis developed a reproducible computational workflow to identify biologically plausible, antibody-accessible surface proteins from single-cell RNA-sequencing data. The workflow was implemented as a modular Nextflow pipeline integrating single-cell preprocessing, cell-type annotation, donor-level pseudobulk differential expression, UniProt protein mapping, transmembrane-topology prediction, GTEx-based normal-tissue filtering, and external validation. It was evaluated using a human cutaneous squamous cell carcinoma dataset containing tumour and matched normal skin samples. Differential expression was performed within annotated cell populations to retain cellular context. Tumour-upregulated genes were mapped to human membrane proteins, filtered for predicted single-pass Type I or Type II topology with sufficient extracellular domains, and screened for low expression in selected vital tissues using GTEx. This progressively reduced the candidate space to a smaller set of cell-type-specific proteins. Validation included comparison with antibody-related IEDB antigens, therapeutic-target annotations, and extracellular-domain BepiPred predictions. The final candidates represent computational priorities rather than validated therapeutic targets and require experimental confirmation of protein expression, surface localization, antibody binding, functional relevance, and normal-tissue safety. Overall, the framework provides a transparent approach for narrowing transcriptomic data toward experimentally testable cell-surface therapeutic candidates.

Date/Time: 16/10/2026 – 13:30 PM
Examination Committee:
Theodore Dalamagas, Research Director, ATHENA Research Center
Dimitrios Stravopodis, Associate Professor, Department of Biology, National and Kapodistrian University of Athens
Eleni Christodoulou, Scientific Associate, ATHENA Research Center

Teams link: https://teams.microsoft.com/meet/362275300616504?p=SX5AkcQxcBxmfu4aOl