Pietro Grandinetti, Machine Learning Developer in Milan, Metropolitan City of Milan, Italy
Pietro Grandinetti

Machine Learning Developer in Milan, Metropolitan City of Milan, Italy

Member since September 12, 2017
Pietro is a systems engineer with a professional background as a software developer and a PhD in large-scale optimization, from the Technology Institute of Grenoble. Equally at ease in applied science and software positions, he enjoys designing and prototyping systems in Python, R, and C using web and data science tools. He likes new challenges and loves lifelong learning.
Pietro is now available for hire

Portfolio

Experience

Location

Milan, Metropolitan City of Milan, Italy

Availability

Part-time

Preferred Environment

Linux, Django, C, R, MATLAB, Python

The most amazing...

...thing I've built was a distributed, efficient, mixed integer optimization algorithm to design the optimal schedule of traffic lights in large cities.

Employment

  • Software Developer

    2018 - PRESENT
    Motorola Solutions (via Toptal)
    • Worked as a core member of the Python development team.
    • Constructed a data engineering pipeline which included Python, SQL, and Oracle Cloud Commerce.
    • Built a batch processing infrastructure for periodic big-data engineering.
    • Interfaced Python code with different SQL database systems.
    • Created REST APIs and triggers for Cronicle jobs.
    Technologies: REST, SQL, Python
  • Research Scientist

    2018 - 2018
    UCLA
    • Was invited as a core participant in the Long Program: Science at Extreme Scales: Where Big Data Meets Large-Scale Computing.
    • Contributed to state-of-the-art research on big data and machine learning.
    • Defined new directions of industrial and academic research directions in the field.
    Technologies: Large-Scale Computing, Big Data
  • Python Developer

    2017 - 2018
    Gridcell (via Toptal)
    • Developed a RESTful API in a Django REST Framework.
    • Created a WebSocket connection with Django channels.
    • Built the client- and server-side for data streaming through AWS IoT and AWS Kinesis.
    • Developed and queried Elasticsearch indexes via a Python client.
    Technologies: Elasticsearch, Amazon Web Services (AWS), Django Channels, Django, Python
  • Data Scientist

    2017 - 2018
    RankSense
    • Implemented convex and non-convex optimization algorithms for the reranking of web pages.
    • Implemented forecast and prediction of page sales time series.
    • Developed an exploratory analysis of Google Analytics Data.
    • Created large-scale optimal procedures in native scientific Python, with NumPy, and Pandas.
    • Developed deep learning and artificial intelligence (AI) systems for automated search engine optimization (SEO).
    Technologies: Deep Learning, Python, Search Engine Optimization (SEO)
  • Software Developer

    2017 - 2018
    LegalKite
    • Built a REST API for LegalKite.com with a Django-REST framework.
    • Scraped Swiss law code and stored the data using PostgreSQL which allowed the creation of personal annotations.
    • Supported search optimization with Elasticsearch through a database of laws and annotations.
    • Deployed the system entirely into AWS facilities (RDS, Elasticsearch service, and EC2).
    • Wrote deep learning and latent semantic indexing (LSI) algorithms for text classification.
    Technologies: Amazon Web Services (AWS), PostgreSQL, Elasticsearch, Django
  • Software Developer

    2017 - 2017
    Bluesophy
    • Created a chatbot that acts as recommendation system.
    • Incorporated smart search methods in the existing database with a Django ORM.
    • Installed a syntactic analysis of input sentences, using NLTK.
    • Integrated the bot within a standard web architecture (Angular and Django).
    • Generated human-friendly answers with a hyperlink redirection to favorite items.
    Technologies: Natural Language Processing (NLP), Django, Python
  • Research Engineer

    2014 - 2017
    CNRS
    • Created a microscopic simulator of a road traffic network in Grenoble (FR), using the Aimsun simulator.
    • Designed a centralized and distributed control algorithms for intelligent traffic lights.
    • Integrated numerical optimization procedures into the Aimsun architecture.
    • Created simulative comparisons with an industrial-strength controller.
    • Developed a big-data analysis of microscopically generated road traffic data.
    Technologies: Aimsun, MATLAB, Python

Experience

  • Katie DJ (Development)
    https://github.com/pgrandinetti/katiedj

    Katie DJ is the first free traffic data broadcast. It is a web platform that streams data related to road network traffic (e.g., number of vehicles in the streets). The data is open and accessible to everyone via WebSockets without needing any other type of information such as email.

  • Road Network Macrosimulator (Development)
    https://github.com/pgrandinetti/traffic-macrosimulator

    The project is an open-source MATLAB package that can be used to create and simulate road networks objects described by a macroscopic dynamics. The technical details can be explored further in my PhD thesis.

  • LegalKite (Development)

    LegalKite is the first legal annotation platform for professionals of the Swiss legal industry. I developed part of the back-end; the main goal was the optimization of the search through laws and annotations, and I am now developing deep learning models for NLP in the legal domain.

  • Plume | Compiler Learning Language Tool (Other amazing things)
    https://github.com/pgrandinetti/compilers

    This is a new, minimalist programming language that I built from scratch. I didn't use any automated tool or parser (i.e., I wrote every line of code). The language was designed as a tool to learn compiler theory.

    I spoke about the theory and practice of compilers, as well as how I built Plume, in a series of articles published at the link below.
    • https://pgrandinetti.github.io/compilers

Skills

  • Languages

    R, Python, Java, C, SQL
  • Frameworks

    Django, Django Channels
  • Tools

    MATLAB
  • Paradigms

    Data Science, Search Engine Optimization (SEO), REST
  • Other

    Freelance Developer, Optimization, Deep Learning, Machine Learning, Natural Language Processing (NLP), Aimsun, Big Data, Large-Scale Computing
  • Libraries/APIs

    Pandas
  • Platforms

    Amazon Web Services (AWS), Linux
  • Storage

    PostgreSQL, Elasticsearch, Oracle DBMS

Education

  • PhD degree in Optimization of Large Scale Networks
    2014 - 2017
    Grenoble Institute of Technology - Grenoble, France
  • Master's degree in Automation Engineering
    2011 - 2013
    University of Calabria - Rende, Italy
  • Bachelor's degree in Computer Engineering
    2008 - 2011
    University of Calabria - Rende, Italy

Certifications

  • TensorFlow in Practice
    NOVEMBER 2019 - PRESENT
    Deeplearning.ai via Coursera
  • Data Science
    JUNE 2018 - PRESENT
    Johns Hopkins University via Coursera
  • Deep Learning
    JANUARY 2018 - PRESENT
    Deeplearning.ai via Coursera
  • Machine Learning
    SEPTEMBER 2014 - PRESENT
    Stanford University via Coursera

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