UX Researcher & Product Designer · Systems Thinker
I explore how people understand complex products, make decisions, and interact with technology. Through research and systems thinking, I turn scattered information into clear, evidence-based design decisions.
About
I didn't start my career in UX.
For more than a decade, I worked in industrial design and product development, collaborating with engineers, manufacturers, and cross-functional teams to bring products from concept to production. That experience taught me to see products as systems, where technology, people, and processes continuously influence one another.
When I moved into UX Research, I realized that the same way of thinking applies to digital products. Instead of understanding how physical components fit together, I became interested in how people make sense of complex systems, how they make decisions, and how thoughtful design can reduce uncertainty.
Today, I research complex products, uncover patterns in user behavior, and transform research into structures that help teams design with greater clarity and confidence.
Whether I'm evaluating an enterprise platform, improving a user workflow, or exploring Human-AI Interaction, I'm driven by the same question:
How can complexity become something people can confidently use?
Where I've Worked
Supporting enterprise UX research by improving how research findings are organized, understood, and translated into better product decisions.
Evaluated an AI-supported knowledge system for source reliability and trust, then redesigned its configuration to make research insights more traceable and evidence-based.
Led cross-functional product development for automotive components, bridging design intent, engineering constraints, and production realities across complex team structures.
Designed mechanical components for automotive systems from initial concept through technical documentation, working closely with engineers and production teams throughout.
Case Studies
A selection of projects where research, systems thinking, and design come together to solve real problems.
Evaluated and redesigned an internal AI agent at TeamViewer | focusing on source grounding, hallucination prevention, and knowledge boundary acknowledgment.
View ProjectFull UX design process for a mobile app helping international students navigate their first weeks in Germany | from 4 interviews and 38 survey responses to high-fidelity prototype.
View ProjectModerated usability evaluation with 8 participants using think-aloud protocol | identified issues across list creation, scan feedback, search relevance, and language accessibility.
View ProjectDesigned a controlled experiment measuring how AI explainability affects user trust, perceived usefulness, and intention to use conversational AI interfaces.
View ProjectContact
I'm currently seeking full-time UX Research or Human-AI Interaction roles in Germany and Europe. If you're building AI products that need a researcher who understands both systems and people. Let's talk.
An internal AI agent giving cross-team stakeholders access to UX research insights, without reading full reports. The challenge: making it trustworthy, not just useful.
The Problem
UX research reports were thorough, but their format didn't match how stakeholders engage with knowledge in fast-paced workflows. An AI agent was introduced to bridge this gap, connected to Confluence documentation and answering queries in plain language. The challenge: an agent can sound confident while being completely wrong.
"How reliably and effectively does an AI agent support cross-team access to UX research knowledge through source-grounded responses?"
Research Process
Reviewed the agent's prompt structure | identifying instruction conflicts, scope ambiguities, and conditions leading to hallucinated responses across multiple testing cycles.
Seven representative stakeholder queries assessed against four criteria: source citation, Confluence traceability, response relevance, and knowledge boundary acknowledgment.
Direct exposure to stakeholder reactions revealed where the agent helped and where trust broke down in real usage.
Key Findings
Impact
Stakeholders retrieved source-linked excerpts in one interaction instead of navigating multiple reports | enabling research to influence fast-moving product decisions.
The most important insight: an agent that prioritises transparency over fluency builds more durable trust. Acknowledging knowledge limits is more valuable than generating confident-sounding answers.
Full Project Report
If you would like to read the complete practical project report, you can open the full PDF below.
Open Full PDF ReportA full UX design process | from 4 qualitative interviews and 38 survey responses to dual-mode hi-fi prototype | for a mobile app easing the overwhelming first weeks of student life in Germany.
Overview
International students arriving in Germany face bureaucracy, housing searches, university systems, and community-building | all simultaneously, in a new language. Existing tools are fragmented and institutional. Startklar provides task-based guidance with a community layer, meeting students at the moment they need help.
Full Case Study Document
A moderated usability study with 8 participants using think-aloud protocol identified critical failure patterns across list management, barcode scanning, and price visibility — and proposed targeted redesigns for each.
EDEKA is one of Germany's largest grocery retailers. Its app is used daily by millions — but usability testing revealed a persistent gap between what users expected and what actually happened, particularly during list creation, product scanning, and purchase planning.
Usability issues in consumer apps are often trust issues in disguise. When users receive no feedback, they don't just get frustrated — they question whether the app is working at all.
Users landed directly on an existing list instead of a list overview, leading them to delete items and reuse the same list. The multi-list feature remained undiscovered throughout sessions.
No feedback during barcode scanning caused repeated attempts and confusion. Users assumed the feature was broken rather than simply slow, reducing trust in the entire app.
Price information was absent during list creation, making it impossible to plan spending in advance. Users returned to paper lists where they felt more in control.
Introduce a list overview as the default entry point, allowing users to immediately view all existing lists instead of landing directly on a previously used one. This surfaces the multi-list feature and lowers cognitive load during task execution.
Redesign the scanning experience to provide clear guidance and continuous feedback throughout the interaction. Show active scanning state, confirm when an item has been successfully added, and build confidence in the reliability of the feature.
Integrate price information directly into the shopping list experience, allowing users to view individual item prices while building their list and understand their overall spending. This enables informed decisions during the planning phase.
The most important insight from this project: usability issues in consumer apps are frequently trust issues in disguise. When the system provides no feedback, users don't just feel uncertain about the feature — they begin to doubt the entire product.
This project reinforced that feedback loops are not a nice-to-have. They are the foundation of a trustworthy experience. Every interaction without feedback is a missed opportunity to build user confidence.
A controlled experiment and survey measuring how AI explainability affects user trust, perceived usefulness, and intention to use conversational AI interfaces | applying constructs from HCI literature.
Context
This project investigated how explainability in conversational AI interfaces affects user trust | testing whether users who receive transparent AI reasoning report higher trust, perceived usefulness, and intention to use compared to those receiving opaque responses.
"Explainability is not just a transparency feature | it's a trust mechanism that shapes whether users act on AI-generated information."
Research Process
Defined trust, perceived usefulness, and intention-to-use constructs from established HCI literature | ensuring measurement validity and theoretical grounding.
Designed a controlled experiment with two conditions (explainable vs. non-explainable AI responses) and a LimeSurvey questionnaire to measure outcomes across constructs.
Coordinated participant recruitment, random assignment to conditions, and data collection | ensuring methodological rigor and clean condition separation.
Connection to Broader Work
This experiment directly informed my thesis direction | examining how knowledge-boundary acknowledgment in AI systems shapes perceived credibility. The same trust mechanisms at play in conversational explainability also appear in enterprise AI knowledge agents, and in the prompt redesign work at TeamViewer.