Academic and independent work

Research & Teaching

Established work in machine learning and speech, university teaching, and early-stage independent research into distributed intelligence and human–AI coordination.

Established Research

From 2008 to 2013, I worked as a research associate in machine learning and multimodal systems at TU Braunschweig. My work focused on iterative, information-theoretic approaches to multi-modal and multi-channel speech recognition.

I coauthored six peer-reviewed publications. Our paper On Iterative Exchange of Soft State Information in Two-Channel Automatic Speech Recognition received the best-paper award at the 10th ITG Symposium on Speech Communication.

The work included externally funded research and industry collaborations. I also supervised student researchers and served as the elected representative of the institute’s academic staff.

Teaching

I have been a guest lecturer at Hochschule Furtwangen University since 2020. I develop and teach courses that connect conceptual foundations with practical work:

  • AI Ethics: ethics frameworks, regulatory approaches including the EU AI Act and NIST AI Risk Management Framework, corporate responsibility, and ethical trade-offs;
  • Human-Centric Conversational AI: user experience, psychology, and hands-on implementation of voice and multimodal conversational systems;
  • Agentic Human–AI Collectives: collective intelligence and hands-on implementation of hybrid human–AI agent systems.

I continue to evolve all three courses as the technology and the surrounding questions develop.

Current Independent Work

My current research explores Active Inference, distributed intelligence, and the coordination of human–AI collectives. I am interested in how adaptive systems maintain coherence without eliminating autonomy, and whether ideas from collective intelligence can inform the design of multi-agent systems and the organizations around them.

This work is at an early stage. I have developed a conceptual scaffold and a first toy simulation, and I am working toward a more formal treatment and publishable experiments. These ideas have not yet been peer-reviewed or established through publication; they should be understood as active research questions rather than settled results.

The practical motivation is to develop better ways of thinking about agency, coordination, and accountability as AI systems become participants in products, teams, and institutions—not merely tools used within them.