Research rarely follows a straight path. Looking back at my academic journey from 2010 to today, I can trace a clear thread connecting distributed computing at CERN, data strategy consulting, visual analytics, and my current focus on AI-powered disinformation detection. Here is that story.

My research career began at CERN, working on the LCG Persistency Framework (CORAL, COOL, POOL) — the middleware that manages petabytes of physics data across the Worldwide LHC Computing Grid. As part of the CERN IT Department’s Database Services group, I contributed to the framework that enables thousands of physicists worldwide to access and analyze data from the Large Hadron Collider.

This work was published in the Journal of Physics: Conference Series:

• Valassi, A; Clemencic, M; Dykstra, D; Frank, M; Govi, G; Kalkhof, A; Loth, A; Nowak, M; et al. (2011). LCG Persistency Framework (CORAL, COOL, POOL): Status and Outlook. J. Phys.: Conf. Ser. 331, 042043.
• Trentadue, R; Clemencic, M; Dykstra, D; Frank, M; Kalkhof, A; Loth, A; Nowak, M; et al. (2012). LCG Persistency Framework (CORAL, COOL, POOL): Status and Outlook in 2012. J. Phys.: Conf. Ser. 396, 052067.

I also presented CORAL at the EGI User Forum, demonstrating how the framework handles cloud resource allocation across distributed infrastructure:

Working at CERN taught me fundamental lessons about data at scale: how to manage, query, and make sense of petabytes of information distributed across hundreds of computing centers worldwide. These lessons would prove invaluable in everything that followed.

After several years in enterprise software, I published a paper on the necessity of modern data strategies in the context of digital transformation:

• Loth, A. (2017). Die Notwendigkeit einer modernen Datenstrategie im Zuge der digitalen Transformation. Information — Wissenschaft & Praxis, 68(1), 75–77.

This paper bridged my technical background with the strategic reality I observed in organizations: many companies were drowning in data but starving for insights. The gap between data collection and actionable intelligence was growing, not shrinking.

Recognizing that data strategy alone was not enough without the ability to see and communicate data effectively, I focused on visual analytics. This led to three books:

• Visual Analytics with Tableau (Apress/Springer, 2019) — now cited 37+ times
• Datenvisualisierung mit Tableau (Springer, 2021)
• Decisively Digital (Wiley, 2021) — on data-driven transformation

The common thread: making complex data accessible to human decision-makers. Whether it was petabytes of physics data at CERN or business KPIs in a boardroom, the challenge was always the same — translating raw information into understanding.

A collaboration with the Haufe Group explored how synthetic control methods could extend ML-based risk controlling:

• Loth, A. (2023). Erweiterung des Machine Learning-gestützten Risikocontrollings mit Synthetic Control. In: Data Driven Controlling, pp. 219–230, Haufe Lexware Verlag.

This work combined causal inference with practical business applications — a stepping stone toward my current focus on detecting manipulation in information systems.

My doctoral research at Frankfurt University of Applied Sciences (supervised by Prof. Martin Kappes) focuses on how generative AI is weaponized for disinformation — and how we can detect it. Key publications:

• Loth, A., Kappes, M., & Pahl, M.-O. (2024). Blessing or Curse? A Survey on the Impact of Generative AI on Fake News. arXiv:2404.03021. (64 citations)
• Loth, A. et al. (2026). Industrialized Deception at Scale. ACM WWW ’26.
• Loth, A. et al. (2026). The Verification Crisis. ACM WWW ’26.
• Loth, A. et al. (2026). Eroding the Truth-Default. ACM WWW ’26.
• Loth, A. et al. (2026). Origin Lens. ACM WebSci ’26.

In just two years, this work has accumulated 184+ citations on Google Scholar and produced the CRED-1 dataset on Hugging Face.

Looking back, the through-line is clear: making sense of complex, distributed information systems and ensuring their integrity.

• At CERN: ensuring data integrity across a global computing grid
• In data strategy: ensuring organizations can trust their data foundations
• In visual analytics: ensuring humans can correctly interpret what data tells them
• In AI disinformation research: ensuring society can distinguish truth from fabrication

The tools change. The scale changes. But the fundamental question remains: Can we trust what we see?

You can find my full publication list on Google Scholar, ORCID, and my Research page.