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Achievement: Pull Sharkx2Achievement: Quickdraw

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annelisesaive/README.md

Hi! I’m Anne-Lise, a neuroscience PhD and applied data scientist.

I build measurement + analysis pipelines that turn real-world human data (behavior, sensory experience, text) into interpretable outcomes — and translate findings into product-ready insights for personalized health and nutrition.

Pinned projects:

  • DeepFooding — quantify eating behavior from meal videos (pose tracking → events/features)
  • Memorable Scents — Predictive modeling of odor memory (ML & Stats)
  • Chatbot_Wine_Memory — semi-structured interviews to decode and characterize food/wine experiences
  • Wine_tasting_KG — clean ~130k reviews and build a knowledge graph linking descriptors, regions, and ratings

Links: LinkedIn | Substack | Google Scholar

Pinned Loading

  1. DeepFooding-CDL DeepFooding-CDL Public

    DeepFooding turns meal videos into quantitative eating-behavior metrics using pose/keypoint tracking (e.g., DeepLabCut) plus confidence-aware post-processing.

    Jupyter Notebook 1

  2. Chatbot_Wine_Memory Chatbot_Wine_Memory Public

    An adaptive chatbot for conducting semi-structured interviews on memorable wine experiences, capturing sensory details, settings, emotions, and personal significance.

    Python

  3. Wine_tasting_KG Wine_tasting_KG Public

    Wine_tasting_KG cleans and structures ~130k Wine Enthusiast reviews into a dataset and knowledge graph linking wines, tasting descriptors, regions and ratings for exploratory analysis.

    Jupyter Notebook 1

  4. jltt-grc/memorable_scents jltt-grc/memorable_scents Public

    Code and notebooks for the Memorable Scents project: analyzing odor-evoked memories and modeling what makes a scent memorable.

    Jupyter Notebook 2