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?

My transition from healthcare data to micro-mobility was shaped by both personal necessity and professional growth. After spending years in highly regulated data environments—managing cancer research and integrating patient data—I developed a deep expertise in bioinformatics and machine learning systems for sensitive information. Cycling became a personal refuge for me, helping me process a difficult loss and find renewed daily joy outside the clinical world. The pandemic accelerated this change: I started creating videos about e-bike and scooter innovations and quickly realized that principles like data privacy, UX design, and machine learning—skills I honed in healthcare—could have a real, tangible impact in micromobility. To formalize this new direction, I took on freelance work and partnered with brands such as VanMoof, Cowboy, and Niu, seeing them not as competitors but as collaborators driving the whole sector forward. The iOS application I am now developing brings together these experiences; it uses on-device ML models to analyze ride patterns, predict maintenance, and foster community across brands—essentially translating predictive analytics and outcome modeling from my bioinformatics background into safer, more connected cycling experiences.

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?

The big challenge with IoT-enabled e-bikes is avoiding what I refer to as the "VanMoof trap": over-engineering connectivity to the point where bikes simply don't work if the company’s servers go down—a reality that left 190,000 customers stranded after VanMoof’s bankruptcy. Having worked directly with both VanMoof and Cowboy, I’ve learned that successful IoT integration requires a measured approach. Core functions like unlocking, pedal assist, and basic settings should always work offline, via Bluetooth or physical controls, while online features should enhance—not restrict—the riding experience. Battery drain is an often-overlooked usability issue: continuous GPS tracking can reduce range by 15–30%, cellular connectivity by another 5–10%, and together these can cut the advertised range by a quarter, forcing users to choose between smart features and actually reaching their destination. My approach is to build in graceful degradation, so bikes remain fully functional if the company disappears, servers go offline, or a rider just prefers analog simplicity. The industry is coming to realize that riders want thoughtful intelligence—features like theft recovery and crash detection are worth the connectivity trade-off, but too many customization options, social features, or gamification elements often create friction instead of value.

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?

The intersection of healthcare and mobility UX highlights a major shift: moving away from overwhelming users with upfront consent forms and toward contextual, progressive privacy models that foster trust through transparency. As I moved from compliant healthcare systems to GDPR-focused mobility platforms, I noticed that users now expect much more control—feature-by-feature toggles instead of a simple opt-in or opt-out, with clear explanations provided right when data is requested. This mirrors how we approached patient consent in healthcare, where we explained the specific uses of different types of medical data. Research backs up what I’ve seen in both fields: apps that openly show their data collection practices and sponsorships are viewed as more credible than those that hide this information, regardless of what data is actually collected. Take Buffer’s example—making “Leave Buffer” a prominent menu item reflects what I learned in healthcare: respecting user autonomy by making it easy to delete data actually increases user retention because it shows you believe in the value you provide. My current iOS app builds on these principles, with just-in-time permission requests, anonymous data structuring by default, and on-device ML processing that keeps sensitive patterns local. Only aggregated insights are shared to the cloud, applying the same data minimization techniques I used in bioinformatics, where we separated clinical operations from research analytics.

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?

Achieving true sustainability in micromobility means leaving behind the reliance on proprietary components—a trap that contributed to VanMoof’s downfall, with €77.8 million lost against €65.6 million in revenue, partly because of warranty repair costs for parts that simply couldn’t be fixed. Through my work with different brands, I’ve seen firsthand that repairability isn’t just a nod to environmentalism—it’s a business imperative. Companies that design modular batteries, for example, allow individual cells to be swapped out in ten minutes, bringing replacement costs down from €200–700 for a full battery to just €5–10 for cell swaps, all while reducing the carbon footprint by 70%. The EU Battery Regulation, which mandates removable and replaceable batteries by February 2027, will drive this shift across the industry, but the smart designers are already adopting open standards, publishing repair guides, and designing for easy disassembly right from the start. My experience with component failures has shown that the top brands use automotive-grade connectors, IP-rated housings that allow for service access, and firmware that doesn’t lock out third-party parts—treating e-bikes as repairable infrastructure rather than disposable gadgets. Companies like rebike in Germany and Roetz in the Netherlands, whose subscription and refurbishment models extend product lifecycles, prove that circular economy thinking is a real competitive advantage when performance is measured by total lifecycle value instead of just the initial sale.

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?

Machine learning is no longer just experimental in micromobility—it’s become essential. For example, Barcelona’s bike-sharing system now achieves 80% accuracy in predicting brake pad replacements, using DeepSurv models trained on 53 million trips. These are the same types of survival analysis algorithms I previously used in bioinformatics to predict patient outcomes. My current iOS app brings these worlds together, using on-device ML to classify ride types, predict maintenance needs, and provide personalized recommendations with clear confidence scores. Soon, natural language processing will help parse user feedback more effectively, filtering out boilerplate responses to get to real pain points. The real breakthrough is not just in prediction accuracy, but in interpretability: using SHAP values to explain why the model recommends a brake service in 20 days, for instance, builds trust in the same way it’s required in healthcare, where black-box algorithms are heavily scrutinized. My experience working with brands using computer vision for sidewalk detection and route optimization shows that effective ML co-creation is a continuous loop: algorithms suggest improvements based on real usage, engineers implement changes, and new data either confirms or challenges those ideas, driving true product evolution. What I’ve learned from healthcare is that machine learning succeeds when it enhances human decision-making with transparent, understandable reasoning—instead of replacing judgment with a “black box”—whether we’re predicting cancer outcomes or fine-tuning e-bike maintenance.

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?

The shift from brand-driven authority to community validation has dramatically reshaped innovation in micromobility. Today, 71% of buyers are influenced by social media and forums like Endless Sphere, where users are often cautioned to rely on long-term ownership experiences instead of polished YouTube marketing. My own experience making content during COVID reinforced this: viewers place their trust in creators who share honest, ongoing experiences—including component failures and real comparisons between brands I work with—rather than in sponsored reviews that avoid criticism. Collaborating with multiple brands as partners, not rivals, has helped me spot trends like battery degradation within 1–2 years or specific wear issues on hub motors—problems that manufacturers might downplay, but that communities will keep spotlighting until they're addressed. The transparency paradox is clear here: when I openly disclose brand partnerships while highlighting real issues—like VanMoof’s server dependencies or Niu’s Bluetooth quirks—my credibility actually increases, because viewers recognize genuine documentation over marketing gloss. Maintenance protocols shared in forums and videos often prove more valuable than official manuals; routines like monthly tire checks, chain lubrication every 100 kilometers, or keeping batteries at a 50–80% charge become community wisdom that carries more weight than technical spec sheets, ultimately pushing brands to refine their products based on real feedback or risk reputational harm that no marketing budget can fix.

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?

For investors, the smart move is to focus on companies with sustainable unit economics—not just rapid growth at any cost. The 73% year-over-year decline in investment is a sign that the market has learned from stories like Bird’s: a fast track to unicorn status means little without real profitability. It’s important to look for operators achieving actual EBIT profitability, like Veo, rather than relying on “adjusted EBITDA” and creative accounting. The most meaningful technical differentiation isn’t about piling on features, but about building defensible intellectual property around core challenges. For example, swappable battery systems—like Gogoro’s, which handles 390,000 daily swaps across 12,200 kiosks—address real operational needs while generating ongoing revenue through battery-as-a-service models. Computer vision solutions from companies like Drover AI help win regulatory permits, and predictive maintenance systems that cut downtime by 20–30% provide clear value to operators. The geographic landscape is also shifting: India has attracted $620 million in investment as electric two-wheelers head toward a 60–70% market share, while Europe’s infrastructure and sustainability regulations are creating fertile ground for circular economy models. Having seen VanMoof’s collapse up close, I advise evaluating whether products can function without company servers, use standardized components for repair, and allow data portability if the company fails. The real winners will be those who bring the discipline of regulated industries like healthcare—privacy-first architecture, open community engagement, and patient, long-term capital—aiming for steady growth (the projected 13–16% CAGR through 2035), rather than trying to conquer the entire $245 billion market overnight.