Daniel Burfoot, Natural Language Processing (NLP) Developer in Berkeley, CA, United States
Daniel Burfoot

Natural Language Processing (NLP) Developer in Berkeley, CA, United States

Member since June 8, 2017
Daniel is an experienced software engineer, data scientist, and NLP researcher with an expertise in Java and Python programming and a Ph.D. in machine learning. He has worked with Hadoop, the AWS cloud, SQL databases (MySQL/PostgreSQL), front web programming in HTML/JavaScript, machine learning algorithms, TensorFlow, and more.
Daniel is now available for hire


  • Ozora Research
    AWS EC2, Azure Machine Learning, Natural Language Processing (NLP)...
  • Cargo Chief
    Amazon Web Services (AWS), AWS, Flask, MySQL...
  • User Testing
    Keras, TensorFlow, Natural Language Processing (NLP), Python



Berkeley, CA, United States



Preferred Environment

Amazon Web Services (AWS), AWS, Linux, Python, SQL, Jakarta EE

The most amazing...

...project I've built is a combined sentence parser and text compressor; the former finds the parse tree that produces the shortest code length for the latter.


  • Founder

    2014 - PRESENT
    Ozora Research
    • Developed machine-learning algorithms for sentence parsing and modeling.
    • Designed, developed, and performance-tuned back-end SQL databases.
    • Worked on the user interface and visualization for the system’s admin console (JavaScript and HTML5).
    • Worked on DevOps to enable the code to run on Linux instances on the AWS cloud (S3, EC2, RDS, and Spot Market).
    • Designed the software architecture in Java to ensure that all the pieces interacted smoothly.
    Technologies: AWS EC2, Azure Machine Learning, Natural Language Processing (NLP), PostgreSQL, Java
  • Python Developer

    2018 - 2018
    Cargo Chief
    • Developed algorithms in Python to extract truck information (location, truck type, and so on) from email text; the challenge lay mainly in the widely varying text structure.
    • Built a suite of evaluation, management, and analysis tools for the system using MySQL, EC2, and CI tool.
    • Created an admin web app console in Flask to help developers control, analyze, and debug the core NLP components.
    Technologies: Amazon Web Services (AWS), AWS, Flask, MySQL, Natural Language Processing (NLP), Python
  • NLP Consultant

    2017 - 2018
    User Testing
    • Helped to develop an NLP system to detect sentiment in user experience narration transcripts; used Python and Keras.
    • Took on the main challenge which was the limited amount of available training data; a key insight was how to use information from other datasets to help with our problem.
    • Created a visualization tool that used the neural network to highlight key phrases of strong sentiment.
    Technologies: Keras, TensorFlow, Natural Language Processing (NLP), Python
  • Lead Scientist

    2011 - 2014
    • Worked as the primary developer of a big data audience analysis system.
    • Programmed Hadoop, using native Java SDK, to process big data from real-time ad exchanges.
    • Developed a system to connect the Hadoop output to a machine learning algorithm.
    • Built a visualization/analysis back-end in MySQL to enable clients to understand the audience profile and characteristics.
    • Integrated the audience analysis system with other components of the company's stack (the bidder system and the operations console).
    • Wrote additional significant ETL code in Java for the company's reporting system.
    Technologies: Machine Learning, Amazon Elastic MapReduce (EMR), AWS EC2, AWS S3, MySQL, Hadoop, Java
  • Software Developer

    2009 - 2010
    Rodale Press (Contract)
    • Developed SmartCoach and SmartCoachPlus—an automated training program generator for runners.
    • Programmed the initial version in JavaScript, the second version primarily in Java/JSP.
    • Developed a MySQL back-end for a second version.
    • Implemented complex training program generation rules.
    Technologies: JavaScript, Java


  • Flow Diagram

    To use this tool, a developer first writes an algorithm or software process using a special set of conventions. Then the user can automatically extract a visual diagram describing the algorithm.

    The diagram is very useful for documentation purposes; other developers (or the original developer, at a later point in time) can easily understand the way the code works just by looking at the diagram, without needing to dive into the specific details.

  • Ozora Research Sentence Parser

    At Ozora Research, I built a broad grammar sentence parser without using labeled training data (almost all other work in the area of parsing depends on labeled "treebank" data).

    The parser is built in combination with a specialized text compressor which compresses text by using a parse tree. The parser produces the tree that will produce the smallest code length for the given sentence. You can demo the parser at the link provided.

  • Notes on a New Philosophy of Empirical Science

    This is a book that I wrote about a new approach to empirical science based on lossless data compression. In this philosophy, a researcher proposes a theory, builds the theory into a data compressor, and measures the quality of the theory by invoking the compressor on a large shared data set. If the theory achieves a lower net code length (including the size of the compressor itself) than previous theories, it is confirmed as the new "champion" theory.

    This philosophy guided my work at Ozora Research. In this case, the relevant data set was English newspaper text. To compress this data, I developed theories of grammar and syntax, and build those theories into a data compressor.

  • Statistical Modeling as a Search for Randomness Deficiencies | Ph.D. Thesis

    My Ph.D. thesis developed an approach to statistical modeling based on the search for randomness deficiencies in an encoded form of the data.

    According to algorithmic information theory, if a given model is a perfect fit for a data set, then when you encode the data using the model, the resulting encoded data (typically a bit string) is completely random. This implies that if you have a model—and encode the data using the model and find a randomness deficiency in the encoded data—then there is a flaw in your model. Furthermore, an analysis of the randomness deficiency illustrates a way to improve the model.

    The thesis developed a suite of machine learning algorithms that work by using this idea.


  • Languages

    Java, SQL, Python, XML, JavaScript
  • Other

    Natural Language Processing (NLP), Machine Learning, AWS
  • Frameworks

    Hadoop, Flask
  • Paradigms

    Object-oriented Design (OOD)
  • Platforms

    Linux, AWS EC2, Jakarta EE, Amazon Web Services (AWS), JEE
  • Storage

    MySQL, PostgreSQL, AWS S3, JSON
  • Libraries/APIs

    Keras, React, TensorFlow
  • Tools

    Azure Machine Learning, Amazon Elastic MapReduce (EMR)


  • Ph.D. in Machine Learning
    2006 - 2010
    University of Tokyo - Tokyo, Japan
  • Master of Science in Artificial Intelligence
    2004 - 2006
    McGill University - Montreal, Canada
  • Master of Science in Physics
    2002 - 2004
    University of Connecticut - Storrs, CT, USA
  • Bachelor of Arts in Applied Math and Computer Science
    1995 - 1999
    Harvard University - Cambridge, MA, USA

To view more profiles

Join Toptal
Share it with others