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Achievements

Achievement: Pull Sharkx2Achievement: StarstruckAchievement: Arctic Code Vault Contributor

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JanaJarecki/README.md

Science from data. Not any data, but complex data about human cognition and behavior. 10+ years of statistical coding, ML model development, and empirical study design; diverse data like sensor data, simulations, experiments, observational registry data (RWD), electronic records (EHR), digital application usability studies (UX), discrete choice data, A/B-tests, and web-scraped data. Advanced statistics, machine learning, and AI to forecast human-centered outcomes and behaviors like learning, preference, risky choice, medical choice, perceptions, and health application usage. As such, I am passionate about data.

My work

  • 20+ peer-reviewed publications, the majority first-authored, many a team effort with great collaborators
  • 10+ open-source data sets including machine-readable meta-data and semi-automated data documentation
  • 3 statistical software libraries, including machine-learning and automated reporting (R statistics)
  • 1 platform for psychometric cognitive testing (Python to interface with Otree)

The languages I use most for

Top Langs Top Langs

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  1. Cognitivemodels Cognitivemodels Public

    An R software package to write, train, tune, test, and compare machine-learning models of cognition

    R 26 4

  2. Psychometric-testing-platform-human-cognition-probabilistic-decisions Psychometric-testing-platform-human-cognition-probabilistic-decisions Public

    Production code for adaptive behavioral choice studies on human cognition in probabilistic situations (aka decisions under risk).

    Python

  3. Risk-preferences-and-risk-perception-affect-the-acceptance-of-digital-contact-tracing Risk-preferences-and-risk-perception-affect-the-acceptance-of-digital-contact-tracing Public

    A Bayesian feature-selection model identifying barriers to digital health device adoption during Covid-19. Proposal for strategies to increase uptake

    R

  4. The-influence-of-sample-size-on-preferences-from-experience The-influence-of-sample-size-on-preferences-from-experience Public

    A comparison between Bayesian and heuristic machine-learning models that learn probability information from experience under uncertainty.

    PostScript

  5. A-framework-for-building-cognitive-process-models A-framework-for-building-cognitive-process-models Public

    A conceptual framework for developing formal models of cognitive processes and a systematic review of 116 cognitive models

    R

  6. Naive-and-robust-class-conditional-independence-in-human-classification-learning Naive-and-robust-class-conditional-independence-in-human-classification-learning Public

    A new Bayesian model, DISC-LM, that adapts the naive Bayes assumption of class-conditional feature independence to efficiently learn new categories

    R