Emad Armiti

Braunschweig, Niedersachsen, Deutschland Kontaktinformationen
609 Follower:innen 500+ Kontakte

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Productive and motivated team player. Holding proven knowledge of Data Science, Machine…

Aktivitäten

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Berufserfahrung und Ausbildung

  • NEW YORKER

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Bescheinigungen und Zertifikate

Ehrenamt

  • Software Developer

    SETS - Smart Evolution Technology & Solutions

    – 4 Monaten

    Education

    - Teach middle-school students the Python programming language.
    - Train students in building simple robots using “Micro:bit” chip.

Kurse

  • Creative Thinking workshop instructed by Northwestern University (Chicago Illinois)

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  • Data Analysis - One Million Arab Coders program

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Projekte

  • Auto-enriching OSM data in Palestine using AI techniques

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    - Get aerial images of Palestine from Geomolg.
    - Use Deep Learning models to identify building footprints from aerial imagery. Using a solution for Open Cities AI Challenge: Segmenting Buildings for Disaster Resilience.
    - Compute the location of buildings on the earth (geo-location of the polygons).
    - Build scripts for converting detected polygons to OSM file structure, and push them to OSM.

  • Community detection over YouTube channels

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    - Crawl comments and subtitles for YouTube channels.
    - Subtitles preprocessing (Tokenization, Stopwords Removal, Lemmatization, Stemming, TF-IDF Transformation).
    - Topic modeling, building LDA (Latent Dirichlet Allocation) model using transformed subtitles.
    - Get user topic distributions using the LDA model through their comments.
    - Build the user graph based on topic distribution similarity, and use Stanford Snap Bigclam for visualization.

  • Demand Forecast for e-scooter business

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    Analysis of E-scooter data and recognizing patterns to predict future behavior.
    Skills used: Hyper-parameters tuning, Cross-validation, Ensemble Methods (Gradient Boosting trees), Feature
    Importance and Interpretation, Feature Engineering, and EDA.

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  • Detecting garbages on the street sides

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    A computer vision algorithm that could detect trash from images that are geotagged, and present them on a map. Using Mask R-CNN trained on a dataset consisting of almost 1400 crowdsourced images containing trash.

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  • Personalized place search engine based on places popularity on Twitter

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    Building a full-stack place search engine using data extracted from OpenStreetMap and re-ranking places according to user location and place popularity in social media, by analyzing tweets with NLP-Sentiment Analysis.
    Using: Machine-Learned Ranking, NLP(Sentiment Analysis), Elasticsearch, Docker, MongoDB, Flask, and DevOps (CD - Heroku).

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  • Spatial analysis of public transportation data to estimate bus-stop locations

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    - Get the GPS data for buses from GTFS/GTFSR bundles.
    - EDA and Geo-visualization using GeoPandas, Shapely, Folium.
    - Feature engineering, utilizing the OSM data.
    - Skills used: Spatial concepts (spatial lag, projections, buffers, spatial indexes), GeoPandas, shapely, folium.

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Auszeichnungen/Preise

  • 2nd place in the DMZ startups competition

    -

    2nd place in the DMZ competition with a start-up idea called Palisites.

    Palisites is a virtual tool guide with an interactive map that shows the famous landmarks and the most visited tourist sites, providing brief information about each site and landmark using AR technology when the app accesses the user's camera and recognizes a certain landmark or site.

Sprachen

  • Arabic

    Muttersprache oder zweisprachig

  • English

    Fließend

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