Behavioural Data Analysis
Identifying ADHD-specific behavioural patterns using quantitative data and structured observation.
Pattern Recognition
Attention Metrics
Habit Trends
Co-Founder
Kamil Maruszak
Research Lead at MyndMap
Kamil leads data analysis and research at MyndMap, focusing on transforming behavioural, cognitive, and usage patterns into clear, actionable insights for ADHD-focused tools. With a technical foundation in data science and applied 3D vision/graphics, he brings analytical precision and cross-disciplinary perspective to MyndMap's research efforts.
Professional Profile
Data science, behavioural analysis, and evidence-based ADHD research
Kamil specialises in behavioural data analysis, evidence-based ADHD research, and quantitative evaluation of user patterns. His work connects raw behavioural signals with scientific context, ensuring that MyndMap's ADHD OS features are grounded in clarity, data integrity, and measurable outcomes.
Blending data science with experience in 3D vision and graphics, he approaches complex problems with a structured, technical mindset—balancing scientific rigor with practical product relevance.
Research & Analytical Expertise
Key areas of focus in ADHD data analysis
ADHD Research Integration
Translating academic findings and clinical evidence into product-aligned insights for system refinement.
Neurodiversity
Cognitive Load
Behavioural Science
Feature Validation
Ensuring MyndMap features align with research-backed methodologies and user outcome data.
Data Integrity
Clinical Alignment
Iterative Testing
Research Philosophy
Guiding principles behind Kamil's analytical approach
Kamil believes that meaningful insights come from clarity, precision, and context. For ADHD, data is not just a collection of numbers, but a way to better understand how individuals think, behave, and navigate their environment.
His philosophy prioritises ethical handling, accurate interpretation, and continuous refinement. Research should empower individuals — giving them understanding rather than overwhelming them.
Key Contributions to MyndMap
Major analytical contributions to the ADHD OS
Behavioural Data Framework
Analytical Structure
Designed the behavioural analysis framework used to identify ADHD-specific trends.
Evidence-Based Feature Review
Clinical Consistency
Validated MyndMap's features to ensure research relevance.
User Outcomes Data Evaluation
Behavioural Measurement
Analysed performance outcomes to identify behavioural impact and improvement.
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When approached with intention, data becomes more than numbers — it becomes understanding.