Matthew Woods, Machine Learning Developer in San Jose, CA, United States
Matthew Woods

Machine Learning Developer in San Jose, CA, United States

Member since May 14, 2020
For the past 12 years, Matthew has been creating applied machine learning and data-driven engineering projects in multiple industrial sectors including biotechnology, cybersecurity, and automotive. He is passionate about developing core technologies for diagnostic, adaptive, predictive, and personalized applications.
Matthew is now available for hire


  • SAIC Innovation Center
    Amazon Web Services (AWS), OpenCV, AWS, TensorFlow, Python
  • Venafi
    MLlib, Linux, Unix, AWS EC2, AWS S3, RStudio Shiny, R, Flask, Scikit-learn...
  • Pfizer
    MATLAB, R, Statistics, Machine Learning


  • Science and Mathematics 20 years
  • Time Series 17 years
  • Unsupervised Learning 17 years
  • Supervised Learning 17 years
  • Machine Learning 17 years
  • Signal Processing 17 years
  • Python 7 years
  • Modeling and Simulation 6 years


San Jose, CA, United States



Preferred Environment

Amazon Web Services (AWS), AWS, Trello, Git, PyCharm, Unix, MacOS

The most amazing...

...thing I have developed is a machine learning software that produced the first place winning models in the MAQC competition.


  • Senior Machine Learning Engineer

    2018 - 2019
    SAIC Innovation Center
    • Developed a driver monitoring system fusing video and biometric streams.
    • Developed a system to predict a driver's intended destination.
    • Developed a system to anticipate drivers' environmental control preferences.
    Technologies: Amazon Web Services (AWS), OpenCV, AWS, TensorFlow, Python
  • Senior Data Scientist

    2015 - 2017
    • Developed an anomaly detection system for PKI certificates using Spark and Python.
    • Developed software to organize customers' internal PKI certificates into functionally meaningful groups with hierarchical clustering and a customized domain name similarity metric. Built a stand-alone REST API for this back end using Flask.
    • Developed a system to assign a score to certificates on the basis of revocation likelihood as estimated with machine learning.
    Technologies: MLlib, Linux, Unix, AWS EC2, AWS S3, RStudio Shiny, R, Flask, Scikit-learn, Spark, Python
  • Senior Research Scientist I

    2008 - 2010
    • Developed a machine learning system for the prediction of antibody thermal and acidic stability on the basis of primary sequence with the aim of identifying stability improving inducible mutations.
    • Identified common biological activity among a large panel of compounds with unsupervised learning and computer vision applied to digital microscopy.
    • Performed text mining and natural language processing of a large corpus of miRNA-related publications.
    Technologies: MATLAB, R, Statistics, Machine Learning


  • Neural Network and Bioinformatic designs for Predicting HIV-1 Protease Inhibitor Resistance (Development)

    Doctoral work.

    I created a new machine learning method for online-learning of continuous-valued multi-dimensional to multi-dimensional maps, a novel feature selection method, and a general-purpose protein-encoding scheme for ML applications. These methods are used to personalize the treatment of HIV-positive patients.


  • Other

    Machine Learning, Science and Mathematics, Statistics, Modeling and Simulation, Supervised Learning, Unsupervised Learning, Time Series, Signal Processing, AWS, Computer Vision, Differential Equations, Software Development, Sensor Fusion, Sensor Data
  • Languages

    Python, R
  • Libraries/APIs

    TensorFlow, OpenCV, Keras, Scikit-learn, MLlib
  • Tools

    PyCharm, Git, Trello, MATLAB
  • Platforms

    MacOS, Unix, Amazon Web Services (AWS), AWS EC2, Linux
  • Frameworks

    Spark, Flask, RStudio Shiny
  • Storage

    AWS S3


  • Ph.D. in Cognitive and Neural Systems
    2001 - 2007
    Boston University - Boston, MA, USA
  • Bachelor's Degree in Physics and Mathematics
    1992 - 1996
    University of Michigan - Ann Arbor, MI, USA

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