Geronimo Bergk

Applied AI under Real-World Constraints | Communication & Sensing Systems

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Managing Consultant – Data Science, Horváth AG (Munich)

I work on machine learning systems that must operate under hard real-world constraints — limited energy, data, and compute — while remaining reliable and accountable in practice. My focus is translating learning algorithms into deployable systems, where robustness, evaluation, and system integration matter more than peak benchmark performance.

I am currently a Managing Consultant in Data Science at Horváth, where I design and deploy data-driven decision systems in large-scale enterprise environments. Previously, I was a Research Associate at the Fraunhofer Heinrich Hertz Institute (HHI), working on machine-learning-based forecasting, telemetry, and control for communication systems under operational constraints. I developed reproducible simulation tools and large-scale datasets for optical networks, integrated learning components into real-time system demonstrators, and contributed to benchmark-driven evaluation pipelines. This work resulted in seven peer-reviewed publications at leading venues including OFC, ECOC, and JOCN, and was recognized with the Fraunhofer HHI Emerging Scientist Award for an outstanding master’s thesis.

Engineering principle

My work is guided by a system-level perspective, combining technical excellence and ownership to build learning-enabled systems that operate reliably under real-world constraints.

Research interests

  • Edge and embedded learning systems under resource constraints
  • Human-centered sensing and biosignal processing
  • Edge intelligence for communication-constrained and wireless sensing systems
  • System-level evaluation beyond offline benchmarks

Applied industry experience

  • Data-driven decision systems in finance and operations
  • Large-scale forecasting and simulation under uncertainty
  • Generative-AI–based reporting systems for executive decision-making
  • ML systems with strong requirements on robustness, interpretability, and governance

Education

  • M.Sc. Electrical Engineering, 2021 Technische Universität Berlin
  • B.Sc. Electrical Engineering, 2017 Technische Universität Berlin