Founder and CEO of TCR App Mobility — an ML-based deep tech startup from the area of road traffic safety and precision medicine (website, pitch deck, video presentation).

 

Over 9 years of experience in the development and commercialization of innovative solutions, gained during cooperation with stakeholders from 17 countries on 4 continents. Over 6 years of experience in the field of artificial intelligence (e.g., ANNs, deep learning, data mining), proven by successful validation and real-world implementation of advanced mathematical models.

 

Finalist of prestigious (domestic and international) startup competitions, conventions and festivals. Participant of distinguished business acceleration programs (e.g., Creative Destruction Lab AI Stream, INSEAD AI Venture Lab, Polish Development Fund's School of Pioneers). Exhibitor at more than 20 global tech conferences (e.g., MWC Barcelona, GITEX Dubai, South Summit Madrid, Salon des Inventions Geneva).

 

Seasoned machine learning engineer with extensive experience in physics, mathematics and data analysis. Award-winning architect of deep neural networks and multi-modal probabilistic algorithms with practical market application.

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RESEARCH

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ML-based prediction of road traffic accidents caused by tiredness and inattention (2021–2023)

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The mathematical models of TCR App Mobility are rooted in two years of scientific research with novel findings about human cognition — integrating VR simulations (with non-invasive functional neuroimaging), real-world trials and neuroscience-related experiments. Each study participant was equipped with our mobile application for several weeks prior to entering the driving simulator with EEG, fNIRS and eye tracking sensors. This workflow allowed us to precisely correlate actual brain activity with physiological signals and behavioural anomalies manifested while operating a vehicle. Our proprietary dataset (one of the largest of its kind globally) includes digital health phenotypes of individuals from diverse demographic backgrounds, encompassing a broad range of driving attitudes, lifestyles and ageing profiles.

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TCR App Mobility is implemented as a fully autonomous mobile application (agentic AI) with a proprietary data flywheel and a unique multi-modal dataset of digital biomarkers (focused on attention stability and high-dimensional health phenotyping).

 

Presymptomatic medical diagnostics on the basis of subtle driving anomalies (since 2025)

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By integrating a VR-based training workflow with diagnostic-grade feature engineering and our original approach to adversarial inverse reinforcement learning, we have translated complex neurophysiological impairments into measurable drifts of AIRL-derived driving reward functions in our infinite-dimensional feature space (referred to as a 'diagnostic space'). It enables us to detect neurodegenerative disorders (as well as chronic non-neurological conditions like diabetes, arthritis and glaucoma) 4 to 8 years ahead of the most stringent clinical protocols as of 2026.

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In the case of Parkinson's disease, the process we identify is not the resting tremor — a symptom which will remain imperceptible for another 5 years due to homeostatic plasticity. Instead, our AIRL-derived driving reward functions provide a mathematical representation of how the patient's brain allocates its resources at the most fundamental level (e.g., controlling lane position, managing speed, and reacting to external stimuli while masking early-stage neurodegenerative deficits through compensatory axonal sprouting). Even subclinical micro-damage to dopaminergic neuronal structures manifests as a discernible signature in the longitudinal drift of these proprietary analytical constructs in our diagnostic space, allowing us to capture the functional impact of the first 5–10% of neuronal degeneration. Our approach ensures objective diagnostic accuracy that is immune to demographic variances in driving attitudes and ageing traits.

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TCR App Diagnostics embeds a clinical-grade tool in a consumer-grade device. We are turning the world's road infrastructure into a neurological screening lab — identifying Parkinson's disease approximately 5 years before the first tremor, using solely a smartphone.

 

Personalization of foreign language learning materials on the basis of eye behaviour and face expression analysis (2023–2024)

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Our mathematical model has integrated both convolutional neural networks and Transformers. At the beginning, biometric data (eye movement, pupil dilation, blinking traits, face expressions) were collected as research participants engaged with diverse learning materials. The dataset was labelled on the basis of learning outcomes (like task performance or retention rates). The CNN is used to extract memory-related parameters from eye-tracking and face expression observations, capturing visual indicators of optimal cognitive engagement. Meanwhile, the Transformer is employed to handle temporal dependencies (modelling how they evolve over time), reflecting dynamic changes in effectiveness of knowledge acquisition. The combined CNN–Transformer architecture was trained with participation of volunteers from varied demographic backgrounds. Following this, our model was validated on a separate dataset to assess its generalization ability.

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NOTABLE DISTINCTIONS

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With TCR App Mobility

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Acceptance into Creative Destruction Lab AI Stream (San Sebastian, 2025/26)​

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Graduation from INSEAD AI Venture Lab (2025)

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Top 16 Polish startups (including Top 3 Polish startups from the sector of modern economy) at the 16th European Economic Congress (2024)

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Top 20 European startups at Infoshare Contest (2024)

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Classification among Deep Tech Pioneers by Hello Tomorrow (2024 and 2025)

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Top 20 startups at Mobileheroes Global (2024 and 2025)

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Top 25 startups at OIST Innovation Accelerator (2024)

 

With other startups

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Top 16 Polish startups (including Top 4 Polish startups from the sector of business processes) at the 17th European Economic Congress (2025)

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Winner of the 7th Edition of the Polish Development Fund’s School of Pioneers (2024)​

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EXPERTISE AND RESPONSIBILITIES

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Artificial intelligence

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Deep neural networks (e.g., RNNs, CNNs, PNNs, Transformers), machine learning (supervised, unsupervised, reinforcement), generative AI, RAG, MCP, fine-tuning, LLMs, agentic AI

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Data science

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Data mining, statistics, data processing, cloud computing (AWS, GCP), data structures, data visualization, data transmission, data architecture design​

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Software development

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C/C++, Python (e.g., PyTorch, TensorFlow, Matplotlib, NumPy, SciPy, scikit-learn), mobile applications (Flutter), web platforms (Django), workflow automation and deployment

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Mathematical modelling

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Algorithm development, analysis, testing and reviews

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Scientific research​

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Data analysis, research planning, documentation

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Business

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Startup management, business presentation, technology validation, marketing, pilots, customer acquisition, venture incubation/acceleration, fundraising