alexloth.com

From CERN Grid Computing to AI Disinformation Research: My Academic Journey

โ€”

by

Alexander Loth
in , ,

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.

2010โ€“2013: Grid Computing at CERN

My research career began at CERN, working on the LCG Persistency Framework (CORAL, COOL, POOL) โ€” 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:

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

Demonstration of CORAL at the EGI User Forum (2012)

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.

2017: The Data Strategy Imperative

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

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.

2018โ€“2021: Visual Analytics and Data Literacy

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:

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 โ€” translating raw information into understanding.

2023: Machine Learning for Risk Controlling

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

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

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

2024โ€“2026: AI and Disinformation

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

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

The Common Thread

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.