Academic background

Teaching & Skills

Areas of teaching and student research support, alongside methods and tools developed through academic and software work.

Teaching subject areas

Teaching areas come from the curated academic profile dated 14 August 2026. They describe past and ongoing teaching-related experience, not an independently verified current-semester course assignment.

  • Data Science
  • Multivariate Analysis
  • Persuasive Technology
  • Machine learning and predictive-analytics projects

Research and technical competencies

Competencies describe areas of practical or research experience; they are not proficiency certifications or an exhaustive software inventory.

  • Machine learning and AI

    Deep learning, Transformer models, representation learning, transfer learning, domain adaptation, ensembles, and explainable AI.

  • Time-series and biosignal analysis

    Preprocessing, segmentation, feature extraction, temporal models, leakage-aware validation and reproducibility.

  • Evidence synthesis

    Systematic and scoping reviews, PRISMA-style screening, structured extraction and quality assessment.

  • IoT and experimental sensing

    ESP32 sensing, environmental monitoring, cloud telemetry, DAQ/sensor integration and Graphtec GL900 experimental workflows.

  • Higher education and digital systems

    Quality assurance, accreditation, curriculum support, KPI analytics, data warehousing, and WordPress/CMS web development.

The research overview and publication index provide examples of my scholarly work.