Q1. You've worked across research, technology, and now connected mobility—could you start by sharing how your journey from healthcare UX to e-bike innovation came about?

After a decade managing bioinformatics pipelines and integrating patient data for cancer research in HIPAA-compliant environments, I discovered that survival analysis algorithms, privacy architectures, and consent frameworks perfected in healthcare could solve critical challenges in adjacent IoT sectors. My transition to micromobility wasn't abandoning healthcare, it was proving that healthcare's rigorous methodologies create competitive advantages in any data-sensitive industry.

I now apply the same ML models that predict patient outcomes to forecast component failures with 80% accuracy. I translate EHR consent management protocols directly to IoT device permissions and use clinical decision support UX principles to guide users through complex connected features. When major e-bike manufacturers faced server dependency crises stranding 190,000 users, I provided solutions using healthcare's graceful degradation architectures, ensuring critical functions operate during network outages.

By applying medical-grade standards to the €245B micromobility market, I’m demonstrating how healthcare’s advanced approach to data governance, privacy-by-design, and explainable AI can be a real differentiator, beyond just compliance.

 

Q2. As e-bikes become smarter through IoT integration, what are the biggest design or usability challenges you see in balancing connectivity with rider simplicity?

Healthcare's painful EHR implementations taught me that feature maximalism kills adoption, physicians abandon systems with 200+ menu options despite sophisticated capabilities. I've witnessed this same pattern when over-engineered connectivity left thousands of connected bikes unusable after vendor bankruptcy.

The parallels I leverage are striking: I address connectivity draining 25% of e-bike battery using the same frameworks I developed for mobile health apps during critical monitoring. I translate solutions for clinical alert fatigue directly to IoT notification overload. My solution is to apply healthcare’s frameworks; graceful degradation, selective data collection, transparent resource use.

Three principles guide me:

• Core functions must work offline (like emergency systems in hospitals).
• Only collect data that demonstrably improves outcomes.
• Make resource consumption transparent and manageable.

It’s basically taking healthcare’s “first, do no harm” and applying it to IoT: enhance the experience without creating dependencies. Devices stay functional even if servers fail or users prefer simplicity.

 

Q3. You've worked closely on user experience design in healthcare and mobility—how do you see UX principles evolving as more devices collect personal data in real time?

I’ve implemented GDPR Article 9 compliance for special category health data, moving systems from 30-page consent forms to granular, just-in-time permissions. This boosted comprehension by around 40%.

I bring the same approach to IoT: replace huge terms-of-service dumps with contextual, progressive disclosure. For example, location is only requested when activating theft tracking, and we explain the direct benefit. The transparency paradox from clinical trials —where open discussion of data use increased participation by 25%— applies here too.

Key patterns I follow:

• Just-in-time consent with clear value explanation
• Granular control per feature
• On-device processing that keeps sensitive patterns local

These lessons from HIPAA compliance now guide how I approach any system handling real-time personal data, from genomics to location tracking.

 

Q4. The e-bike and micromobility sector is rapidly adopting sustainability goals. How do you think product designers and technologists can realistically merge performance, repairability, and eco-consciousness?

Healthcare taught me that single-use devices aren’t sustainable. Reprocessable equipment is both safer and cheaper. The same applies to e-bikes: proprietary, unrepairable systems create huge costs. When a manufacturer lost €77.8M against €65.6M revenue due to unrepairable components, it was the same trap I’ve seen in medical devices.

I apply three sustainability principles:

• Open standards reducing costs by 70%
• Modular architectures for component-level repair
• Standardized interfaces supporting third-party service

For example, swapping individual battery cells costing €5–10 versus replacing the full battery at €200–700 mirrors how we extended surgical instrument lifecycles. Healthcare’s circular economy principles translate directly to sustainable micromobility design.

 

Q5. With AI now influencing both product testing and user feedback analysis, do you see a future where machine learning can meaningfully co-create or improve consumer tech experiences?

I take clinical ML pipelines like Cox Proportional Hazard models and DeepSurv neural networks —used for patient outcomes— and apply them to predictive maintenance. The same survival analysis that forecasts cancer recurrence predicts component failures, with 80% accuracy given sufficient data.

I also bring healthcare’s explainable AI approach to consumer apps: SHAP values that explain why the model recommends a brake service in 20 days build trust, just like in clinical decision support.

Three patterns I translate:

• NLP from clinical notes to process user feedback
• Adverse event detection → component failure identification
• Continuous learning health systems → iterative product improvement

When I see Barcelona’s bike-sharing predicting brake replacements across 53 million trips, it mirrors oncology decision support. Transparent reasoning, not black-box automation, is the principle I carry across sectors.

 

Q6. Community-driven content seems to be playing a powerful role in shaping brand credibility. How do you see independent creators and reviewers influencing innovation cycles in connected devices?

Healthcare moved from physician-led to patient-participatory medicine, and I apply the same mindset to community-driven innovation. In both cases, transparency builds trust. About 71% of connected device buyers rely on community reviews over marketing.

I document real failures alongside successes, knowing that builds credibility. Community-validated protocols —whether chronic condition care or e-bike maintenance— often prove more practical than official manuals.

My approach:

• Continuously monitor user-reported issues
• Respond quickly to community-identified problems
• Iterate based on real-world evidence

It’s shared decision-making applied to product development. Communities become active partners, not passive consumers.

 

Q7. If you were advising investors entering the connected mobility space, what key indicators or innovations would you tell them to watch for in the next few years?

I evaluate investments like I do healthcare tech: outcomes over vanity metrics. A 73% decline in micromobility investment mirrors lessons from telemedicine, growth without sustainable unit economics fails.

I focus on:

• Graceful degradation architectures
• Data portability per GDPR Article 20
• Standardized components for third-party service
• Defensible IP around operational challenges

The winners combine privacy-by-design, evidence-based iteration, and patient capital. My decade in regulated healthcare markets gives me the framework to assess complex, data-sensitive investments in micromobility, aiming for steady 13–16% CAGR growth rather than chasing the entire $245B market overnight.