Gabriel Bianconi, Predictive Analytics Developer in New York, NY, United States
Gabriel Bianconi

Predictive Analytics Developer in New York, NY, United States

Member since July 23, 2018
Gabriel is a senior machine learning engineer. His experience includes predicting cancer therapeutics (NLP), reducing industrial accidents (computer vision), and optimizing corporate QA (predictive analytics). He holds BSc and MSc degrees in computer science from Stanford, where he conducted award-winning AI research, and lectures at some of the largest conferences in the world, e.g., AWS re:Invent, the annual conference by Amazon.
Gabriel is now available for hire




New York, NY, United States



Preferred Environment

Python, Unix

The most amazing...

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


  • Founder

    2018 - PRESENT
    Scalar Research
    • Founded a consulting firm to help companies tackle complex business challenges with cutting-edge AI and data science.
    • Served in leadership and advisory roles for tech startups, investment firms, and large corporations across multiple industry verticals.
    • Helped Scale AI ($1+ billion tech firm) optimize quality assurance in its distributed data annotation workforce with predictive analytics.
    • Built computer vision systems to track cars and optimize servicing efficiency for client’s pilot with Fortune 500 auto company.
    • Improved Fandom’s page tagging model (ad targeting, content recommendation) with cutting-edge NLP (from 0.35 to 0.557 R at P95).
    • Created a text-mining (NLP) tool to help HelixNano (biotech firm) predict cancer therapeutics from a massive research dataset.
    • Led the development of semantic search and recommendation engine products for a startup that resulted in contracts with Fortune 500.
    • Helped a leading global professional services firm and a large Asian industrial conglomerate launch a computer vision joint venture.
    • Developed tooling for a data science team creating supply-chain analytics software for a major CPG conglomerate in LATAM.
    • Worked on other projects including license plate recognition (OCR) from edge devices, sports tracking and scoring from real-time video, unstructured data extraction from millions of PDF documents, and financial time series forecasting for securities trading.
    • Presented 20+ technical talks and workshops at major international conferences (e.g., AWS re:Invent, the annual event by Amazon).
    • Taught artificial intelligence to non-technical audiences at leading institutions (e.g., a workshop for artists and public at Tate Modern).
    Technologies: TensorFlow, PyTorch, Python
  • Reviewer

    2020 - 2020
    International Conference on Machine Learning (ICML)
    • Selected as a peer reviewer for paper submissions for ICML 2020 (International Conference on Machine Learning), one of the leading academic conferences on machine learning.
    • Reviewed papers related to natural language processing (NLP), model compression, and deep learning for tabular data analysis.
    Technologies: Machine Learning
  • 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 ten papers) at NIPS ML4H 2017.
    • Published a paper in Nature Partner Journals (Nature) Digital Medicine.
    Technologies: Python, TensorFlow, PyTorch
  • Research Intern

    2016 - 2017
    • 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: Python, PyTorch, TensorFlow
  • Software Engineer Intern

    2015 - 2015
    • Implemented monitoring features for Google Cloud.
    • Presented to enterprise customers and 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: C++, JavaScript, Python
  • Software Engineer Intern

    2014 - 2014
    • 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: Java, PHP, JavaScript


  • 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 Li—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.


  • Languages

    Python, JavaScript, SQL, HTML, C++, PHP, Java
  • Libraries/APIs

    PyTorch, TensorFlow, Keras, Sklearn, PySpark
  • Paradigms

    Data Science
  • Other

    Natural Language Processing (NLP), Deep Learning, Computer Vision, Predictive Analytics, Cloud Computing, Quantitative Finance, Back-end, Machine Learning, Front-end
  • Platforms

    Amazon Web Services (AWS), Linux, Google Cloud Platform (GCP), Unix
  • Frameworks

    AWS EMR, Spark
  • Storage

    PostgreSQL, MySQL, Redis


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


  • Toptal Expert Public Speaker

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