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

 

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 7 years of experience in the field of artificial intelligence (e.g., neural networks, 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).

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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 rigorous scientific research with novel findings about human cognition. Participants completed a multi-week longitudinal assessment phase via our SaMD-compliant mobile application, which preceded sessions in the custom-engineered VR driving simulator synchronized with EEG, fNIRS, eye tracking (saccadic latency, fixation precision, microsaccade dynamics), sEMG, GSR, pressure mapping, and vehicle telemetry data. This workflow allowed us to precisely correlate actual brain activity with entangled driving dynamics and subconscious behavioural patterns 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 and ageing profiles.

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TCR App Mobility is implemented as an autonomous mobile application (agentic AI) with a proprietary data flywheel. Our solution emits sound alerts whenever a dangerous data pattern is detected — averting the natural lapses in attention at least three minutes before any discernible cognitive impairment. It ensures sustained awareness throughout the entire journey.

 

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 associated with a positive-definite kernel (referred to as the "diagnostic space"). It enables us to detect neurodegenerative diseases (as well as confounding non-CNS conditions such as diabetes, arthritis and glaucoma) 4 to 8 years before the onset of clinical symptoms.

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Simultaneously, our models can uncover high-entropy anomalies that are associated with early-stage brain tumours, potentially allowing intervention when they are still fully curable.

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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 5–10 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 cortical remapping). 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 with hospital-grade sensitivity (>92%) and specificity (estimated 94%), approximately 5 years earlier than advanced clinical brain scans.

 

Personalization of Foreign Language Learning Materials on the Basis of Eye Behaviour and Facial 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, facial 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 facial 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., RNN, LSTM, CNN, PNN, Transformers), machine learning (supervised, unsupervised, self-supervised, reinforcement), multi-modal data fusion, 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 (e.g., 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 (e.g., Flutter), web platforms (e.g., Django), workflow automation and deployment

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Mathematical modelling

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Algorithm development, analysis, testing and reviews; infinite-dimensional topology

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Scientific research​

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Research planning, cohort selection, data analysis, documentation

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Business

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Startup leadership and management, business presentation, technology validation, marketing, piloting, customer acquisition, venture incubation/acceleration, fundraising