archimedes-Artificial Intelligence, Data Science, Algorithms-greece

 
Artificial Intelligence
 
Data Science
 
Algorithms

[Archimedes Talks Series] Counting Stars is Constant-Degree Optimal For Detecting Any Planted Subgraph

Dates
2024-07-11 17:00 - 18:00
Venue
Microsoft Teams Meeting
Title: Counting Stars is Constant-Degree Optimal For Detecting Any Planted Subgraph

Presenter: Professor Ilias Zadik, Assistant Professor at Yale University


Abstract: Over the last decade, multiple papers have studied the power of low-degree polynomials in detecting subgraphs in random graphs. Formally, all these papers study special cases of the following general hypothesis testing problem: let H=H_n be an arbitrary undirected graph on n vertices. Can a D-degree polynomial detect between a ``null'' Erdős-Rényi random graph G(n,p) and a ``planted'' random graph which is the union of G(n,p) together with a random copy of H=H_n? The special cases include the case when H is a clique (the planted clique model), when  H is a matching (the planted matching problem), and more.
 
We prove a new unifying positive result that for all H=H_n, as long as D=O(1), the optimal such D-degree polynomial is always given simply by the count stars in the input graph. This allows us to prove multiple old and new results in the literature of low-degree polynomials for this task.

Bio: Ilias Zadik is Assistant Professor of Statistics and Data Science at Yale University. His research mainly focuses on the mathematical theory of statistics and its many connections with other fields such as computer science, probability theory, and statistical physics. His primary area of interest is the study of “computational-statistical trade-offs,” where the goal is to understand whether computational bottlenecks are unavoidable in modern statistical models or a limitation of currently used techniques. Prior to Yale, he held postdoctoral positions at MIT and NYU. He received his PhD from MIT in 2019. 


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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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