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Gabriel Bianconi, Python Developer in New York, NY, United States
Gabriel Bianconi

Python Developer in New York, NY, United States

Member since July 23, 2018
Gabriel is a machine learning scientist with experience in applying cutting-edge academic research to solve real-world problems. Previously, he was a B.S. and M.S. student in computer science at Stanford University, where he conducted research in deep learning and computer vision and held positions at Google, Facebook, startups, research labs, and investment firms.
Gabriel is now available for hire

Portfolio

Experience

  • Python, 5 years
  • Cloud Computing, 4 years
  • Machine Learning, 3 years
  • Computer Vision, 3 years
  • SQL, 2 years
  • JavaScript, 2 years
  • Natural Language Processing (NLP), 2 years
  • Quantitative Finance, 2 years
New York, NY, United States

Availability

Part-time

Preferred Environment

macOS and Linux, Python, Git

The most amazing...

...project I've worked on was using machine learning and computer vision to help hospitals improve patient care.

Employment

  • Founder

    2018 - PRESENT
    Scalar Research
    • Worked at Scalar Research, a full-service artificial intelligence and advanced analytics consulting firm.
    • Founded Scalar to bridge the gap between groundbreaking machine learning research and complex business challenges. Our team helps companies understand how they can leverage data to drive outsized improvements to their core metrics.
    • Provided end-to-end services in strategy, R&D, and deployment for data-driven products and solutions.
    • Helped companies develop their in-house capabilities with private workshops, lectures, and educational materials.
    • Worked for various clients ranging from large corporations to startups in diverse industries, such as healthcare, chemicals, education, and software.
    Technologies: Python, PyTorch, TensorFlow
  • Research Assistant

    2017 - 2017
    Stanford Computer Vision Lab
    • Researched for AI-enabled smart hospitals that leverage computer vision methods to reduce the monitoring workload of clinicians.
    • Developed deep learning models (based on RNNs and CNNs), end-to-end data pipeline, and annotation tools.
    • Published a first-author paper that was selected for a spotlight presentation (top 10 papers) at NIPS ML4H 2017.
    Technologies: PyTorch, TensorFlow, Python
  • Research Intern

    2016 - 2017
    QxBranch
    • Researched quantum deep learning methods using the D-Wave 2X quantum computer.
    • Developed a novel architecture and quantum-assisted learning algorithm for convolutional deep belief networks.
    • Accepted for a poster presentation at the Adiabatic Quantum Computing Conference 2017 (Tokyo, Japan).
    Technologies: TensorFlow, PyTorch, Python
  • Software Engineer Intern

    2015 - 2015
    Google
    • Implemented monitoring features for Google Cloud.
    • Presented to enterprise customers.
    • Was featured on the Google Cloud Platform Blog.
    • Conducted market research, prepared mockups, and created proof of concept for upcoming Google Cloud Monitoring features.
    Technologies: Python, JavaScript, C++
  • Software Engineer Intern

    2014 - 2014
    Facebook
    • Developed collaboration and data integrity features to streamline the workflow of large customers using Facebook Ads Manager.
    • Created a plugin for an internal tool to increase developer productivity.
    Technologies: JavaScript, PHP, Java

Experience

  • AI in Healthcare Research (Development)

    During his master's program, Gabriel conducted research at the Stanford Partnership in AI-Assisted Care, a joint lab between the Stanford Computer Science Department (Prof. Fei-Fei Liā€”Chief Scientist of Cloud AI/ML at Google) and the Stanford School of Medicine (Prof. Arnold Milstein). His research focused on improving clinical care and reducing monitoring costs in hospitals by leveraging machine learning and computer vision, and resulted in a first-author manuscript selected as Top 10 Research Paper at the NIPS Machine Learning for Health 2017 Workshop.

  • Quantitative Trading for Cryptoassets (Development)

    Gabriel built an algorithmic and quantitative trading platform for crypto-asset markets that handled over US$10 million in volume. He developed quantitative strategies based on techniques from signal processing, control theory, machine learning, and blockchain analysis, and created a crypto portfolio optimization framework with convex optimization. Using these tools, he managed a portfolio of hundreds of thousands of dollars for his investors and outperformed both Bitcoin and the Coinbase Index.

  • Quantum Deep Learning Research (Development)

    Gabriel was one of ten students to graduate with honors in computer science in his undergraduate class at Stanford. His thesis investigated quantum deep learning algorithms using NASA's D-Wave quantum computer, and was selected for a presentation at the AQC 2017 Conference in Tokyo, Japan.

Skills

  • Languages

    Python, JavaScript, SQL, C++, C
  • Frameworks

    Machine Learning
  • Other

    Deep Learning, Computer Vision, Cloud Computing, Natural Language Processing (NLP), Quantitative Finance, Financial Modeling
  • Libraries/APIs

    TensorFlow, PyTorch
  • Platforms

    Amazon Web Services (AWS), Linux
  • Storage

    Google Cloud, PostgreSQL, MySQL, Redis

Education

  • Master of Science degree in Computer Science
    2017 - 2018
    Stanford University - Stanford, CA, USA
  • Bachelor of Science degree in Computer Science
    2013 - 2017
    Stanford University - Stanford, CA, USA
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