About & FAQ

Who is Dimitrios Panagopoulos?

Dimitrios Panagopoulos is a Data Science Leader and PhD Mathematician based in Athens, Greece. He works as a Manager at EY Greece in the Data Analytics & AI practice and serves as a part-time Lecturer of Machine Learning and AI at a joint postgraduate programme in Business Mathematics run by the National and Kapodistrian University of Athens and the Athens University of Economics and Business.

What is his academic background?

He holds a PhD in Mathematics (February 2009), an MSc in Pure Mathematics (November 2003), and a degree in Mathematics (September 1999), all from the Department of Mathematics of the National and Kapodistrian University of Athens. His doctoral and postdoctoral research focused on algebraic topology, group theory, homological algebra, and cryptography.

Has he founded any companies?

Yes. Between 2013 and 2016 he founded Eureka Module, a data science startup focused on analytics and consulting. As Lead Data Scientist he built recommender systems for retail clients and delivered customer segmentation projects using Python, Hadoop, and machine learning techniques.

What teaching experience does he have?

Beyond his current lectureship in the Business Mathematics MSc programme, he has taught mathematics at the University of Ioannina, the Agricultural University of Athens, the Technological Educational Institute (TEI) of Messolonghi, and the TEI of Peloponnese. He also spent over a decade as a freelance mathematics tutor (2008–2016).

What is his expertise in Generative AI?

At EY he has managed teams building graph-enhanced RAG (Retrieval-Augmented Generation) systems, agentic due diligence tools, and early warning systems that use GenAI to process news for banking clients. He has also participated in AI lifecycle strategy and AI system reviews for global NGOs, and teaches GenAI concepts in his academic lectureship.

What is his experience with Graph Data Science and Neo4j?

He is a Neo4j Certified Professional (certified September 2023) and has hands-on experience building graph-enhanced systems in production consulting environments. He writes about Graph Data Science — including Neo4j applications, anomaly detection, and clustering — on his Medium profile.

What is Topological Data Analysis and how does he apply it?

Topological Data Analysis (TDA) uses tools from algebraic topology — such as persistent homology — to analyse the shape of data. Dimitrios has published academic work connecting TDA with clustering algorithms and lazy learning, and has applied these ideas to practical data science problems. His 2022 arXiv paper "Topological data analysis and clustering" is the most accessible entry point to this work.

What industries has he worked in?

His consulting work at EY has focused primarily on financial services — including major Greek and European banks — covering projects in credit risk, customer segmentation, non-performing loan management, hard collection systems, loyalty analytics, and digital channel optimisation. He has also worked with a global shipping company and international NGOs on AI strategy and governance.

What technical tools and languages does he use?

His core technical stack includes Python, R, SQL, and Neo4j for data science and graph work; PySpark, Hadoop, Hive, and Databricks for big data; Azure for cloud; and Power BI and SPSS Modeler for analytics and visualisation. He holds an Azure Fundamentals certification (Microsoft, February 2024) and a Certified Analytics Professional (CAP) credential from INFORMS, recertified in October 2024.

What academic papers has he published?

His peer-reviewed publications and book chapters include:

• Topological data analysis and clustering — book chapter in Analysis, Cryptography and Information Science, World Scientific, 2023. arXiv
• A Short Exposition of Topological Applications to Security Systems — Applications of Mathematics and Informatics to Science and Engineering, Springer, 2014.
• Some aspects of group-based cryptography — Journal of Applied Mathematics and Bioinformatics, vol. 3, no. 1, 2013.
• Groups with Z-torsion Homology — International Journal of Algebra, vol. 6, no. 5–8, 2012.
• A secret sharing scheme using groups — arXiv, 2010. arXiv
• Group elements conjugate to their powers and Bass' conjecture — Homology, Homotopy and Applications, vol. 10, no. 1, 2008, pp. 223–236.

Full list available on his Google Scholar profile and the Writing page.

What does he write about on Substack?

His Substack publication EurekaCodex explores the intersection of mathematics and AI leadership — covering AI governance, the philosophy of data science, and navigating the transition to agentic AI workflows. It is aimed at practitioners and leaders who want strategic depth alongside technical rigour. Read on Substack →

What topics does he cover on Medium?

His Medium articles focus on applied Graph Data Science using Neo4j, anomaly detection with autoencoders, and specialised clustering techniques — with an emphasis on bridging pure mathematical theory with Python-based implementation. Visit Medium profile →

Has he written for general audiences?

Yes. He has contributed opinion pieces on AI and data policy to Greek media, including a 2023 article on the promises and challenges of artificial intelligence published on in.gr, and a 2022 piece on public governance and open data published in To Vima in.gr.

How can I contact Dimitrios Panagopoulos?

The best way to reach him is via LinkedIn. He is open to professional conversations about data science consulting, AI strategy, academic collaboration, and speaking engagements.

Where can I follow his work?

His writing appears on Substack (AI strategy and leadership) and Medium (technical data science). Academic work is indexed on Google Scholar.

Is he available for speaking or consulting?

He has presented at national conferences of the Hellenic Mathematical Society and at military/applied mathematics conferences. For speaking or consulting enquiries, please reach out via LinkedIn.