Filip Noworolnik AI researcher • builder • entrepreneur
From research to real-world AI products

Building practical AI for sports, medicine, and industry.

I’m Filip Noworolnik - a PhD researcher, AI engineer, and commercialization-oriented builder working at the intersection of computer vision, multimodal AI, and applied machine learning. My work spans sports analytics, medical imaging, trustworthy AI, and product-focused R&D.

I care about systems that do more than look good in a paper: they should solve real problems, fit operational workflows, and stand a chance of becoming products, partnerships, or startups.

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About

A researcher who likes to build.

My profile sits between academic R&D, engineering delivery, and venture creation. I enjoy converting ambitious ideas into structured roadmaps, prototypes, experiments, and credible commercialization stories.

Academic depth

I work on advanced AI topics with a strong emphasis on computer vision, multimodal reasoning, and model interpretability. My research background helps me assess what is novel, what is reproducible, and what is actually worth pursuing.

Engineering mindset

I am comfortable with the full path from data and experimentation to implementation details, validation, deployment concepts, and product constraints. I value systems that are robust, practical, and measurable.

Commercial instinct

I naturally think in terms of user pain points, product differentiation, proof of value, TRL progression, and go-to-market logic. I like technology that can leave the lab and survive contact with reality.

Focus areas

Where I do my best work.

These are the domains and problem types where I can contribute the most - either as a researcher, technical lead, consultant, or builder.

Sports analytics & movement intelligence

AI for football and broader sports settings, especially where lightweight capture setups, actionable feedback, and clear user value matter more than lab-perfect conditions. I am particularly interested in turning video into coaching insight.

Shot technique analysis and feedback systems
Single-camera perception and performance assessment
Grassroots-friendly AI products for clubs and academies

Medical AI & healthcare imaging

I work on image-based systems where quality, interpretability, clinical relevance, and deployment realism matter. The strongest themes in my work include dermatology, echocardiography, confocal microscopy, and dental imaging.

Skin lesion and cell analysis
Echocardiography and foundation-model-style workflows
Tooth and root canal segmentation with product potential

Trustworthy & explainable AI

I am interested in systems that help people understand model behavior, spot failure modes, and use AI in a way that earns trust. This includes concept-based reasoning, anomaly awareness, and interpretable representations.

AI commercialization & R&D strategy

Beyond models, I enjoy shaping the path from research result to market-facing opportunity - clarifying the product story, identifying early adopters, defining deliverables, and making the technical direction legible to partners and investors.

Selected projects

A portfolio built around real use cases.

A representative sample of the kinds of systems, products, and research directions I have been developing.

Sports AI Computer Vision Multimodal Feedback

Single-camera football shot analysis

A system for automatically evaluating shooting technique from regular video and producing actionable feedback. The idea combines perception, biomechanics-inspired interpretation, and language-based feedback generation for players at different skill levels.

Medical AI Dermatology Spin-out Potential

Dermatology and skin imaging solutions

Work spanning skin lesion analysis, microscopy-driven cell understanding, and product-minded dermatology AI concepts. The broader goal is to translate strong technical work into clinically meaningful and commercially viable tools.

Cardiac Imaging Foundation Models Healthcare R&D

Echocardiography AI workflows

Exploration of scalable AI workflows for echo video understanding, classification, reporting support, and foundation-model-inspired approaches that could improve generalization and downstream utility in clinical settings.

Dental Imaging 3D SaaS Direction

Dental CBCT segmentation platform

AI-assisted segmentation of teeth and root canals with a product vision centered on dental workflows, interactive visualization, and practical software delivery. A strong example of research meeting vertical software opportunity.

Research & trajectory

From interpretable vision to deployable AI.

My trajectory is shaped by a recurring question: how do we build AI systems that are both technically strong and genuinely useful in the world?

Interpretable computer vision and trustworthy AI

Research focused on concept- and structure-aware methods, spatial reasoning between higher-level visual concepts, and AI systems that do not behave like black boxes by default.

Medical imaging and domain-specific AI products

Translation of advanced AI methods into practical healthcare applications, with an emphasis on segmentation, classification, workflow support, and explainability where trust is essential.

Commercially oriented AI systems for sports, medicine, and industry

Building toward solutions that can become partnerships, demos, deployments, and startups - not only publications. I am especially interested in applied multimodal systems and human-centered decision support.

What I optimize for: technically ambitious work with a believable path to adoption.
Work with me

How I can contribute.

I am most useful in settings where ambitious AI ideas need both technical depth and product realism.

Research collaboration

For universities, labs, hospitals, and R&D teams looking for support in computer vision, multimodal AI, explainability, or experimental design.

AI product strategy

For teams shaping an MVP, demo, grant proposal, spin-out path, or commercialization concept around AI-heavy technology.

Technical advisory

For companies that need an experienced perspective on feasibility, model strategy, dataset planning, evaluation, or roadmap prioritization.