archimedes-Artificial Intelligence, Data Science, Algorithms-greece

 
Artificial Intelligence
 
Data Science
 
Algorithms

[Archimedes Talks] A deep learning-powered right ventricle counterpart of the Simpson’s method of discs and uncertainty quantification

Dates
2025-01-10 13:30 - 14:30
Venue
Artemidos 1 - Amphitheater

Title: A deep learning-powered right ventricle counterpart of the Simpson’s method of discs and uncertainty quantification


Speaker: Archontis Giannakidis, Assistant Professor, School of Science and Technology at Nottingham Trent University (NTU), UK


Abstract: Quantitative evaluation of right ventricular (RV) volumes is of paramount importance in many cardiovascular conditions and is best performed by cardiovascular magnetic resonance imaging (CMR). However, CMR scanners are scarce, costly, and lack portability. Two-dimensional transthoracic echocardiography (2DE) allows for the widely available, low cost and bedside evaluation of RV size and function. 2DE-based quantitative RV analysis is nevertheless restricted by the lack of accurate models of the complex RV shape. In this talk, a feature tokeniser transformer-based model will be presented to calculate the RV end-diastolic (ED) and end-systolic (ES) volumes by relying on tabular data. The proposed method mirrors the Simpson’s method of discs applied to the left ventricular volume calculation task in the sense that it uses area data from various standardised 2DE views (along with age, cardiac phase and gender information) as inputs to the regression model. The pipeline is trained and tested on a small-scale dataset, showing feasibility and promising accuracy.

In the second part of the talk, an instance-based method will be presented for complementing ensemble method-based RV volume predictions with uncertainty scores. The technique will rely on the learned tree structure to identify the nearest training samples to a target instance and then use a number of distribution types to more flexibly model the output. The appropriateness of the proposed framework will be showcased by providing exemplar cases. The estimated uncertainty embodies both aleatoric and epistemic types. This work aligns with trustworthy artificial intelligence since it can be used to enhance the decision-making process and reduce risks. Lastly, the feature importance scores of our framework can be exploited to reduce the number of required 2DE views which could enhance the proposed pipeline’s clinical application.

Bio: Archontis Giannakidis is currently an Assistant Professor in Data Science with the School of Science and Technology at Nottingham Trent University (NTU), UK. Before NTU, Archontis was a Postdoctoral Researcher with the National Heart and Lung Institute at Imperial College London, London, UK (4 years) and the Life Sciences Division at Lawrence Berkeley National Laboratory, Berkeley, CA, USA (3 years). He received his PhD in Electronic Engineering (Inverse Problems) from University of Surrey, UK, where he was advised by Prof. Maria Petrou. He works on the mathematical underpinnings of machine learning and data science, with a special focus on Responsible AI. His research interests lie in the intelligent processing of large amounts of various types of data towards: (i) learning efficient data representations, (ii) revealing hidden patterns in the data, (iii) automating intellectual tasks normally performed by humans, (iv) optimising decision-making, and (v) improving computational modelling of complex systems. Archontis is a member of the special interests group on “Machine Learning and Dynamical Systems” at the Alan Touring Institute. He has gained funding from Innovate UK/EPSRC and the Government Equalities Office, and he holds an international patent for co-inventing a deep learning-based quantitative technique for analysing the right ventricle by relying only on two-dimensional echocardiography.





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Vision

To position Greece as a leading player in AI and Data Science

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Mission

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).

 
 

NEWS

 
Archimedes Workshop on Dialect NLP

Archimedes Workshop on Dialect NLP

Upcoming workshop on Dialect NLP on “Standardization and Variation for Dialect Varieties with Universal Dependencies as an Application Framework” coming up. We are excited to announce that the Dialect NLP team at Archimedes, Athena Research Center, Greece, is organizing a workshop in collaboration with the MaiNLP Research Lab at Ludwig Maximilian University (LMU) of Munich. Workshop details:Workshop info: UniDive WG1 Workshop on Dialect Varieties Dates: September 5–6, 2025 Location: LMU Munich Funded by: UniDive – Universality, Diversity and Idiosyncrasy in Language Technology Archimedes: Dialect NLP and Linguistic Diversity At Archimedes, our Dialect NLP team focuses on the intersection of AI and linguistic diversity, with an emphasis on under-resourced and endangered language varieties, both Greek and non-Greek. Our research includes:   • Dialectal speech-to-text and morphosyntactic modeling  • Dialect-to-standard normalization for Greek varieties   • Intra-dialectal variation analysis   • A critical perspective on annotation frameworks like Universal Dependencies (UD), especially when applied to non-standard or endangered varieties Our resources cover:   • Standard Modern Greek: GUD Treebank   • Greek dialects: Cretan, Messinian, Lesbian, Cypriot, Griko/Greko, Aperathitika   • Non-Greek varieties spoken in Greece: Pomak, Arvanitika All datasets, models, and tools we develop are freely available to the research community. About Our Collaborators at MaiNLP (LMU Munich) The MaiNLP Research Lab at Ludwig-Maximilians-Universität Munich conducts research in Natural Language Processing, combining computer science, linguistics, and cognitive science. Their focus is on human-facing NLP: developing models that are robust to variation, fair, and reflective of human annotation diversity. MaiNLP is also leading the ERC Consolidator Grant project DIALECT, which explores natural language understanding for non-standard languages and dialects—making them an ideal partner in our shared mission to design NLP systems that work for all language varieties. Workshop Goals This workshop will:   • Document current practices in dialectal UD treebanks (e.g., orthographic/phonological variation, lemmatization, interference)   • Identify gaps in annotation and processing across dialects   • Develop shared methodologies for handling variation, improving consistency, and enabling knowledge transfer   • Foster community engagement with researchers working on under-resourced dialects and less-studied language families   • Ground discussions in theoretical insights from Plank (2016): What to do about non-standard language in NLP It is an opportunity to reflect on how annotation frameworks and tools serve—or fall short of serving— dialectal and endangered varieties, and how AI can support documentation, analysis, and preservation of linguistic diversity. We look forward to sharing our findings and engaging with colleagues working on inclusive, culturally informed, and variation-aware NLP. Stay tuned for more updates!

Archimedes and the Biomedical Research Foundation Share Latest Findings in AI and Medicine

Archimedes and the Biomedical Research Foundation Share Latest Findings in AI and Medicine

Archimedes and the Biomedical Research Foundation of the Academy of Athens successfully hosted a special collaborative session at the Panhellenic Working Group Seminars of the Hellenic Society of Cardiology. This session focused on innovative applications of artificial intelligence in medicine, with a particular emphasis on advancements in cardiology. Held on Friday, February 7, 2025, in Room MC2 of the Megaron Athens International Conference Centre, the event brought together leading experts to explore how artificial intelligence is transforming cardiovascular medicine.

Archimedes Reaches Milestone of 200 Publications

Archimedes Reaches Milestone of 200 Publications

Archimedes is proud to announce that its researchers have published over 200 scientific publications in top-tier conferences (NeurIPS, ICLR, ICML) and journals.Archimedes maintains a vibrant scientific community of over 130 researchers, including more than 60 senior researchers (faculty members from Greece and abroad), 12 postdoctoral fellows, and 55 PhD students, along with over 20 undergraduate interns from various disciplines.

 
 

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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